Welcome to Machine Learning

Description: Machine learning is concerned with the development of computer programs that allow computer (or machine) to learn from examples or experiences. Machine learning is of interdisciplinary nature, with roots in computer science, statistics and pattern recognition. In the past decade, this field has witnessed rapid theoretical advances and growing real world applications. Successful applications include machine perception (speech recognition, computer vision), control (robotics), data mining, web search and text classification, time-series prediction, system modelling, bioinformatics, data compression, and many more.

This course will give a comprehensive introduction to machine learning both by presenting technologies proven valuable and by addressing specific problems such as pattern recognition and data mining. This course covers both theory and practices for machine learning, but with an emphasis on the practical side namely how to effectively apply machine learning to a variety of problems. Topics will include

• Supervised learning (of classification and regression functions)

K-nearest neighbors, decision trees, naïve Bayes, support vector machines, logistic regression, evolutionary algorithms, Bayesian Networks, hidden Markov model, neural networks, boosting

• Unsupervised learning and clustering

K-means, hierarchical clustering (agglomerative and divisive), principal component analysis, Expectation Maximization algorithm

• Reinforcement learning

Prerequisites: Basic probability and statistics theory, linear algebra.

Literature:

• Machine Learning – A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012

• Introduction to Machine Learning – second edition, Ethem Alpaydin, The MIT Press, 2009

• Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006

• Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.

Organizer and lecturer: Professor Zheng-Hua Tan, e-mail: zt@es.aau.dk, http://kom.aau.dk/~zt/

ECTS: 3

Time: 29-30 April, 2-3 May and 6 May, 2019, 9:00-16:00

Place:

29-30 April + 2 and 6 May, Fredrik Bajers Vej 7B, room 2-104

3 May, Niels Jernes Vej 14, room 4-117

Zip code: 9220

City: Aalborg Øst

Number of seats: 50

Deadline: 8 April 2019

Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Machine learning is concerned with the development of computer programs that allow computer (or machine) to learn from examples or experiences. Machine learning is of interdisciplinary nature, with roots in computer science, statistics and pattern recognition. In the past decade, this field has witnessed rapid theoretical advances and growing real world applications. Successful applications include machine perception (speech recognition, computer vision), control (robotics), data mining, web search and text classification, time-series prediction, system modelling, bioinformatics, data compression, and many more.

This course will give a comprehensive introduction to machine learning both by presenting technologies proven valuable and by addressing specific problems such as pattern recognition and data mining. This course covers both theory and practices for machine learning, but with an emphasis on the practical side namely how to effectively apply machine learning to a variety of problems. Topics will include

• Supervised learning (of classification and regression functions)

K-nearest neighbors, decision trees, naïve Bayes, support vector machines, logistic regression, evolutionary algorithms, Bayesian Networks, hidden Markov model, neural networks, boosting

• Unsupervised learning and clustering

K-means, hierarchical clustering (agglomerative and divisive), principal component analysis, Expectation Maximization algorithm

• Reinforcement learning

Prerequisites: Basic probability and statistics theory, linear algebra.

Literature:

• Machine Learning – A Probabilistic Perspective, Kevin P. Murphy, The MIT Press, 2012

• Introduction to Machine Learning – second edition, Ethem Alpaydin, The MIT Press, 2009

• Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006

• Pattern Classification, Second Edition, Richard O. Duda, Peter E. Hart, David G. Stork, Wiley Interscience, 2001.

Organizer and lecturer: Professor Zheng-Hua Tan, e-mail: zt@es.aau.dk, http://kom.aau.dk/~zt/

ECTS: 3

Time: 29-30 April, 2-3 May and 6 May, 2019, 9:00-16:00

Place:

29-30 April + 2 and 6 May, Fredrik Bajers Vej 7B, room 2-104

3 May, Niels Jernes Vej 14, room 4-117

Zip code: 9220

City: Aalborg Øst

Number of seats: 50

Deadline: 8 April 2019

Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Zheng-Hua Tan

Welcome to Scientific Computing using Python - High Performance Computing in Python

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into research results as well as easy access to results and improving productivity by reusing code and software.

This is a course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.

This part of the course covers the main area: High performance computing.

High performance computing:

1. High-performance computing and computer architectures

2. Performance optimization

1. Cython (compiled Python via C-extensions)

2. Numba (just in time compilation)

3. f2py (inclusion of Fortran code in Python)

3. Parallel/distributed computing

1. Theoretical aspects (Amdahl's and Gustafson-Barsis' law)

2. Parallel computingon one computer

3. Distributed computing across multiple computers

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed.

Prerequisites: Participants must have some experience in programming Python. If not, there is an introductory course "Scientific Computing Using Python - 1. Python + Scientific Computing". Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages description) in addition to the developed code. The code must include testing/validation, and performance evaluation of parallel computing. An acceptable mini-project and at-least 75% participation is required to pass the course.

Learning objectives: After completing the course the participants will:

1. Know how to use methods and software for performance optimization.

2. know when and how to apply parallel computing for scientific computing.

Organizer and lecturer: Associate Professor Thomas Arildsen, tha@es.aau.dk, Department of Electronic Systems

ECTS: 2

Time: 19-20 November, 2019, 8:30-15:30

Place: Fredrik Bajers Vej 7B, room 2-107

City: 9220 Aalborg

Number of seats: 25 (the course is fully booked. If you wish to be registered on a waiting list, please send an email to Sofie Pia Jensen, spj@adm.aau.dk)

Deadline: 29 October 2019

Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into research results as well as easy access to results and improving productivity by reusing code and software.

This is a course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance-optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.

This part of the course covers the main area: High performance computing.

High performance computing:

1. High-performance computing and computer architectures

2. Performance optimization

1. Cython (compiled Python via C-extensions)

2. Numba (just in time compilation)

3. f2py (inclusion of Fortran code in Python)

3. Parallel/distributed computing

1. Theoretical aspects (Amdahl's and Gustafson-Barsis' law)

2. Parallel computingon one computer

3. Distributed computing across multiple computers

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed.

Prerequisites: Participants must have some experience in programming Python. If not, there is an introductory course "Scientific Computing Using Python - 1. Python + Scientific Computing". Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages description) in addition to the developed code. The code must include testing/validation, and performance evaluation of parallel computing. An acceptable mini-project and at-least 75% participation is required to pass the course.

Learning objectives: After completing the course the participants will:

1. Know how to use methods and software for performance optimization.

2. know when and how to apply parallel computing for scientific computing.

Organizer and lecturer: Associate Professor Thomas Arildsen, tha@es.aau.dk, Department of Electronic Systems

ECTS: 2

Time: 19-20 November, 2019, 8:30-15:30

Place: Fredrik Bajers Vej 7B, room 2-107

City: 9220 Aalborg

Number of seats: 25 (the course is fully booked. If you wish to be registered on a waiting list, please send an email to Sofie Pia Jensen, spj@adm.aau.dk)

Deadline: 29 October 2019

Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Thomas Arildsen

Welcome to Scientific Computing using Python - 1. Python + Scientific Computing

NB This course is one out of two alternates to follow the basic course in Scientific Computing using Python. Link to the other basic course: https://phd.moodle.aau.dk/course/view.php?id=1123

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes itnecessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducibleresearch, which allows other researchers the opportunity to dig further into others research results as well as easy access to results and improvingproductivity by reusing code and software.

This is an introductory course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity inscience due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance optimizingpossibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open sourceand as such an asset for any researcher following the reproducible research paradigm.

The course covers two main areas: i) The Python programming language itself and ii)various aspects of scientific computing. This specific coursecontent is as follows:

The Python language:

1. Course introduction

- Historical overview of scientific computing and high performance computing

2. Python development environment

- Python from above

- Datatypes, built-in functions

- Branching and looping

- Functions (definition, built-in, lambda)

- Module and packages

3. Debugging and testing

- Unittest

- Doctest

- Pdb (breapoints and post-mortem)

4. Basic scientific computing packages

- Numpy (numerical computing - array based - vectorization)

- Scipy (vaious tools for integration, optimization, etc.)

- Matplotlib (data visualization)

- H5py (data storage/access via HDF)

- Documentation using Sphinx

Scientific computing:

1. Basic issues related to computation sciences such as

- Floating-point representation

- Numerical accuracy and condition number

- Cancellation

- Algorithmic complexity

2. Scientific software Development

- Version control (via git)

- Code documentation

- Test procedures (what to test - and how)

- Code refactoring

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for variousscientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not acourse in object-oriented programming and we apply a procedural approach to programming in the course.

Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills ingeneral use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges whenusing parallel computing (but this should not be a problem for this part of the course).

Learning objectives: After completing the course the participants will:

1. have fundamental knowledge of important aspects of scientific computing

2. be able to map a mathematically formulated algorithm to Python code

3. know how to document, debug and test the developed code.

4. know when and how to optimize Python code

Teaching methods: A combination of lectures, demonstrating examples using iPython notebooks, smaller exercises and a mini-project is used tofacilitate learning. The course is rich in examples and active user participation is expected to facilitate learning – the topics covered demand a“learning by doing” approach.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages) in addition to the developed code. The code must includetesting/validation, and performance evaluation. An acceptable mini-project and at-least 75% participation is required to pass the course.

Key literature: We expect to use a combination of the following:

1.Selection of a few chapters in Python books (specified at a later stage)

2.References to Python and all relevant packages (freely available via http://python.org)

3.A number of scientific papers relevant for specific parts of the course.

Organizer and lecturer: Associate Professor Thomas Arildsen, e-mail: tha@es.aau.dk, Department of Electronic Systems

ECTS: 2.5

Time: 12-14 November 2019, from 8:30-15:30

Place: Fredrik Bajers Vej 7B, room 2-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 25 (the course is fully booked. If you wish to be registered on a waiting list, please send an email to Sofie Pia Jensen, spj@adm.aau.dk)

Deadline: 22 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illnessis of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three monthsbefore start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Teacher: Thomas Arildsen

NB This course is one out of two alternates to follow the basic course in Scientific Computing using Python. Link to the other basic course: https://phd.moodle.aau.dk/course/view.php?id=1123

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes itnecessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducibleresearch, which allows other researchers the opportunity to dig further into others research results as well as easy access to results and improvingproductivity by reusing code and software.

This is an introductory course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity inscience due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance optimizingpossibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open sourceand as such an asset for any researcher following the reproducible research paradigm.

The course covers two main areas: i) The Python programming language itself and ii)various aspects of scientific computing. This specific coursecontent is as follows:

The Python language:

1. Course introduction

- Historical overview of scientific computing and high performance computing

2. Python development environment

- Python from above

- Datatypes, built-in functions

- Branching and looping

- Functions (definition, built-in, lambda)

- Module and packages

3. Debugging and testing

- Unittest

- Doctest

- Pdb (breapoints and post-mortem)

4. Basic scientific computing packages

- Numpy (numerical computing - array based - vectorization)

- Scipy (vaious tools for integration, optimization, etc.)

- Matplotlib (data visualization)

- H5py (data storage/access via HDF)

- Documentation using Sphinx

Scientific computing:

1. Basic issues related to computation sciences such as

- Floating-point representation

- Numerical accuracy and condition number

- Cancellation

- Algorithmic complexity

2. Scientific software Development

- Version control (via git)

- Code documentation

- Test procedures (what to test - and how)

- Code refactoring

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for variousscientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not acourse in object-oriented programming and we apply a procedural approach to programming in the course.

Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills ingeneral use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges whenusing parallel computing (but this should not be a problem for this part of the course).

Learning objectives: After completing the course the participants will:

1. have fundamental knowledge of important aspects of scientific computing

2. be able to map a mathematically formulated algorithm to Python code

3. know how to document, debug and test the developed code.

4. know when and how to optimize Python code

Teaching methods: A combination of lectures, demonstrating examples using iPython notebooks, smaller exercises and a mini-project is used tofacilitate learning. The course is rich in examples and active user participation is expected to facilitate learning – the topics covered demand a“learning by doing” approach.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages) in addition to the developed code. The code must includetesting/validation, and performance evaluation. An acceptable mini-project and at-least 75% participation is required to pass the course.

Key literature: We expect to use a combination of the following:

1.Selection of a few chapters in Python books (specified at a later stage)

2.References to Python and all relevant packages (freely available via http://python.org)

3.A number of scientific papers relevant for specific parts of the course.

Organizer and lecturer: Associate Professor Thomas Arildsen, e-mail: tha@es.aau.dk, Department of Electronic Systems

ECTS: 2.5

Time: 12-14 November 2019, from 8:30-15:30

Place: Fredrik Bajers Vej 7B, room 2-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 25 (the course is fully booked. If you wish to be registered on a waiting list, please send an email to Sofie Pia Jensen, spj@adm.aau.dk)

Deadline: 22 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illnessis of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three monthsbefore start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Teacher: Thomas Arildsen

- Teacher: Thomas Arildsen

Welcome to Scientific Computing using Python - 1. Python + Scientific Computing

NB This course is one out of two alternates to follow the basic course in Scientific Computing using Python. Link to the other basic course: https://phd.moodle.aau.dk/course/view.php?id=1124

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into others research results as well as easy access to results and improving productivity by reusing code and software.

This is an introductory course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.

The course covers two main areas: i) The Python programming language itself and ii)various aspects of scientific computing. This specific course content is as follows:

The Python language:

1. Course introduction

- Historical overview of scientific computing and high performance computing

2. Python development environment

- Python from above

- Datatypes, built-in functions

- Branching and looping

- Functions (definition, built-in, lambda)

- Module and packages

3. Debugging and testing

- Unittest

- Doctest

- Pdb (breakpoints and post-mortem)

4. Basic scientific computing packages

- Numpy (numerical computing - array based - vectorization)

- Scipy (vaious tools for integration, optimization, etc.)

- Matplotlib (data visualization)

- H5py (data storage/access via HDF)

- Documentation using Sphinx

Scientific computing:

1. Basic issues related to computation sciences such as

- Floating-point representation

- Numerical accuracy and condition number

- Cancellation

- Algorithmic complexity

2. Scientific software development

- Version control (via git)

- Code documentation

- Test procedures (what to test - and how)

- Code refactoring

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not a course in object-oriented programming and we apply a procedural approach to programming in the course.

Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing (but this should not be a problem for this part of the course).

Learning objectives: After completing the course the participants will:

1. have fundamental knowledge of important aspects of scientific computing

2. be able to map a mathematically formulated algorithm to Python code

3. know how to document, debug and test the developed code.

4. know when and how to optimize Python code

Teaching methods: A combination of lectures, demonstrating examples using iPython notebooks, smaller exercises and a mini-project is used to facilitate learning. The course is rich in examples and active user participation is expected to facilitate learning – the topics covered demand a “learning by doing” approach.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages) in addition to the developed code. The code must include testing/validation, and performance evaluation. An acceptable mini-project and at-least 75% participation is required to pass the course.

Key literature: We expect to use a combination of the following:

1. Selection of a few chapters in Python books (specified at a later stage)

2. References to Python and all relevant packages (freely available via http://python.org)

3. A number of scientific papers relevant for specific parts of the course.

Organizer and lecturer: Associate Professor Thomas Arildsen, e-mail: tha@es.aau.dk, Department of Electronic Systems

ECTS: 2.5

Time: 14-16 May 2019

14 and 16 May, from 8:30 -15:30

15 May, from 9:00-16:00

Place: Fredrik Bajers Vej 7B, room 2-104

Zip code: 9220

City: Aalborg Øst

Number of seats: 50

Deadline: 23 April 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before

NB This course is one out of two alternates to follow the basic course in Scientific Computing using Python. Link to the other basic course: https://phd.moodle.aau.dk/course/view.php?id=1124

Description: Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary to have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into others research results as well as easy access to results and improving productivity by reusing code and software.

This is an introductory course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.

The course covers two main areas: i) The Python programming language itself and ii)various aspects of scientific computing. This specific course content is as follows:

The Python language:

1. Course introduction

- Historical overview of scientific computing and high performance computing

2. Python development environment

- Python from above

- Datatypes, built-in functions

- Branching and looping

- Functions (definition, built-in, lambda)

- Module and packages

3. Debugging and testing

- Unittest

- Doctest

- Pdb (breakpoints and post-mortem)

4. Basic scientific computing packages

- Numpy (numerical computing - array based - vectorization)

- Scipy (vaious tools for integration, optimization, etc.)

- Matplotlib (data visualization)

- H5py (data storage/access via HDF)

- Documentation using Sphinx

Scientific computing:

1. Basic issues related to computation sciences such as

- Floating-point representation

- Numerical accuracy and condition number

- Cancellation

- Algorithmic complexity

2. Scientific software development

- Version control (via git)

- Code documentation

- Test procedures (what to test - and how)

- Code refactoring

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not a course in object-oriented programming and we apply a procedural approach to programming in the course.

Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing (but this should not be a problem for this part of the course).

Learning objectives: After completing the course the participants will:

1. have fundamental knowledge of important aspects of scientific computing

2. be able to map a mathematically formulated algorithm to Python code

3. know how to document, debug and test the developed code.

4. know when and how to optimize Python code

Teaching methods: A combination of lectures, demonstrating examples using iPython notebooks, smaller exercises and a mini-project is used to facilitate learning. The course is rich in examples and active user participation is expected to facilitate learning – the topics covered demand a “learning by doing” approach.

Criteria for assessment: A standard mini-project must be delivered (4-8 pages) in addition to the developed code. The code must include testing/validation, and performance evaluation. An acceptable mini-project and at-least 75% participation is required to pass the course.

Key literature: We expect to use a combination of the following:

1. Selection of a few chapters in Python books (specified at a later stage)

2. References to Python and all relevant packages (freely available via http://python.org)

3. A number of scientific papers relevant for specific parts of the course.

Organizer and lecturer: Associate Professor Thomas Arildsen, e-mail: tha@es.aau.dk, Department of Electronic Systems

ECTS: 2.5

Time: 14-16 May 2019

14 and 16 May, from 8:30 -15:30

15 May, from 9:00-16:00

Place: Fredrik Bajers Vej 7B, room 2-104

Zip code: 9220

City: Aalborg Øst

Number of seats: 50

Deadline: 23 April 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before

- Teacher: Thomas Arildsen

Welcome to Building the Bridge between Electrical Grid Control and Communication in Smart Grids

Description: Smart grids are increasingly becoming a key word describing intelligent ways of controlling the electrical power grid for reasons as efficiency, dependability, future markets and more. In the same time, smart grids are highly cross disciplinary and involve control theory, understanding of the electrical grid, market interaction and communication technologies. To a higher degree, PhD students working with this type of technology is required to have a common understanding of each sub domains, even to the level of terminology. This course aims to bridge the gap between the different disciplines involved that allows PhD candidates to work efficiently with smart grids, with a bias towards communication technologies and its role in smart grids, which until recently has been a hidden player in the smart grid domain.

Organizer: Associate Professor Rasmus L. Olsen, e-mail: rlo@es.aau.dk

Lecturers: Associate Professor Rasmus L. Olsen, Professor Poul Alberg Østergaard, Professor Birgitte Bak-Jensen, Associate Professor Anders N. Andersen, Associate Professor Florin Iov and Associate Professor Jan Dimon Bendtsen

ECTS: 3

Time: 18-22 November 2019, from 8:45-16:15

Place: Fredrik Bajers Vej 7B, room 2-104

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 7 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Smart grids are increasingly becoming a key word describing intelligent ways of controlling the electrical power grid for reasons as efficiency, dependability, future markets and more. In the same time, smart grids are highly cross disciplinary and involve control theory, understanding of the electrical grid, market interaction and communication technologies. To a higher degree, PhD students working with this type of technology is required to have a common understanding of each sub domains, even to the level of terminology. This course aims to bridge the gap between the different disciplines involved that allows PhD candidates to work efficiently with smart grids, with a bias towards communication technologies and its role in smart grids, which until recently has been a hidden player in the smart grid domain.

Organizer: Associate Professor Rasmus L. Olsen, e-mail: rlo@es.aau.dk

Lecturers: Associate Professor Rasmus L. Olsen, Professor Poul Alberg Østergaard, Professor Birgitte Bak-Jensen, Associate Professor Anders N. Andersen, Associate Professor Florin Iov and Associate Professor Jan Dimon Bendtsen

ECTS: 3

Time: 18-22 November 2019, from 8:45-16:15

Place: Fredrik Bajers Vej 7B, room 2-104

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 7 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Anders Nielsen Andersen
- Teacher: Birgitte Bak-Jensen
- Teacher: Jan Dimon Bendtsen
- Teacher: Florin Iov
- Teacher: Rasmus Løvenstein Olsen
- Teacher: Poul Alberg Østergaard

Welcome to Computing with Data using R

Description: Most quantitative research projects involve both case-specific programming tasks as well as data analysis of a more standard nature. When working with quantitative data it is moreover essential to be able to do so in a systematic and reproducible manner with trustworthy software. This course aims at introducing the free program R as a computational environment for integrating these tasks. R has for two decades been a leading tool for computing with data and it is the preferred language for implementation of new statistical methodology.

The course will cover the practice of scientific computing, programming, and quantitative analyses as well as the essential theoretical underpinnings.

The topics of the course will include.

• Introducing R as a statistical programming environment for data analysis.

• Efficient data management using R.

• High-level graphics in R.

• Statistical models in R

• Aspects of scientific computing in theory and practice.

• Reproducible research in practice

• Programming in R and vectorized computations.

• Optional (if time permits) Matrix factorizations and other numerical methods in R. Easy integration of C++ code in R.

IMPORTANT: Prerequisites: Participants must have a working knowledge of elementary statistical methods such as regression models and analysis of variance at the level taught e.g. in the "applied statistics" course described at http://asta.math.aau.dk/eng/

Learning objectives: After completing the course the participants will 1. be able to solve programming tasks with R. 2. know how to manage data, visualize data and fit models to data using R. 3. have learned enough about R to continue learning on their own.

Teaching methods: A combination of instructive videos, computer practicals and lectures.

Criteria for assessment: Active participation in the practicals + approval of major exercise (to be handed in after the course).

Frequently asked questions:

• Q: I am from outside AAU and wish to sign up for the course. What do I do?

A: You click on "signup" and fill out the form.

• Q: I can not get the steps above to work. Who can help?

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

• Q: If I participate in the course, can you then help me analyze a dataset that I work with

as part of my ph.d. project.

A: No, I am afraid that this is not possible

• Q: I would like to participate in the course, but during a part of the course period I can

not be present. Is it possible to follow to course via Skype or similar?

A: No, I am afraid that this is not possible

• Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that

possible?

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

• Q: I realize that I am late for enrollment, but I would really like to participate. Is it

possible.

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

Organizers: Associate Professor Torben Tvedebrink, tvede@math.aau.dk and Associate

Professor Mikkel Meyer Andersen, mikl@math.aau.dk

Lecturers: Associate Professor Torben Tvedebrink, tvede@math.aau.dk and Associate Professor Mikkel Meyer Andersen, mikl@math.aau.dk

ECTS: 4

Time: 9 -10 April, 23 - 24 April and 7 - 8 May 2019, from 9:00 - 16:00

Place: Fredrik Bajers Vej 7C, room 2-209

City: 9220 Aalborg Øst

Number of seats: 40

Deadline: 19 march 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Most quantitative research projects involve both case-specific programming tasks as well as data analysis of a more standard nature. When working with quantitative data it is moreover essential to be able to do so in a systematic and reproducible manner with trustworthy software. This course aims at introducing the free program R as a computational environment for integrating these tasks. R has for two decades been a leading tool for computing with data and it is the preferred language for implementation of new statistical methodology.

The course will cover the practice of scientific computing, programming, and quantitative analyses as well as the essential theoretical underpinnings.

The topics of the course will include.

• Introducing R as a statistical programming environment for data analysis.

• Efficient data management using R.

• High-level graphics in R.

• Statistical models in R

• Aspects of scientific computing in theory and practice.

• Reproducible research in practice

• Programming in R and vectorized computations.

• Optional (if time permits) Matrix factorizations and other numerical methods in R. Easy integration of C++ code in R.

IMPORTANT: Prerequisites: Participants must have a working knowledge of elementary statistical methods such as regression models and analysis of variance at the level taught e.g. in the "applied statistics" course described at http://asta.math.aau.dk/eng/

Learning objectives: After completing the course the participants will 1. be able to solve programming tasks with R. 2. know how to manage data, visualize data and fit models to data using R. 3. have learned enough about R to continue learning on their own.

Teaching methods: A combination of instructive videos, computer practicals and lectures.

Criteria for assessment: Active participation in the practicals + approval of major exercise (to be handed in after the course).

Frequently asked questions:

• Q: I am from outside AAU and wish to sign up for the course. What do I do?

A: You click on "signup" and fill out the form.

• Q: I can not get the steps above to work. Who can help?

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

• Q: If I participate in the course, can you then help me analyze a dataset that I work with

as part of my ph.d. project.

A: No, I am afraid that this is not possible

• Q: I would like to participate in the course, but during a part of the course period I can

not be present. Is it possible to follow to course via Skype or similar?

A: No, I am afraid that this is not possible

• Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that

possible?

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

• Q: I realize that I am late for enrollment, but I would really like to participate. Is it

possible.

A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

Organizers: Associate Professor Torben Tvedebrink, tvede@math.aau.dk and Associate

Professor Mikkel Meyer Andersen, mikl@math.aau.dk

Lecturers: Associate Professor Torben Tvedebrink, tvede@math.aau.dk and Associate Professor Mikkel Meyer Andersen, mikl@math.aau.dk

ECTS: 4

Time: 9 -10 April, 23 - 24 April and 7 - 8 May 2019, from 9:00 - 16:00

Place: Fredrik Bajers Vej 7C, room 2-209

City: 9220 Aalborg Øst

Number of seats: 40

Deadline: 19 march 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Mikkel Meyer Andersen
- Teacher: Søren Højsgaard
- Teacher: Torben Tvedebrink

Welcome to: Advanced signal processing in joint time-frequency and time-scale domains

Description:

The course addresses advanced principles of analysis of information carried by various biological signals. Time or frequency domain analysis often fails to accurately capture the dynamics of non-stationary biological processes modulating the signals recorded by sensors. Basic principles of signal processing such as features extraction and filtering used in the time or frequency domains are extended in the joint time-frequency domain and in the joint time-scale domain. The course is intended to provide the understanding and practical use of the Wigner-Ville, Choi-Williams, Rihaczek, Cone-shaped and Adaptive-kernel quadratic time-frequency distributions, as well as of the wavelet transform with various types of wavelets. The course provides both lectures and hands-on workshops with emphasis on the practical aspects such as advantages and limitations of processing of signals recorded from the brain, muscles, and heart.

Prerequisites:

Basic knowledge in signal processing, mathematics and Matlab.

Learning objectives:

The course aims to provide the students with skills and knowledge in advanced signal processing as a valuable tool in designing of experiments and interpretation of data acquired.

Teaching methods:

Lectures and hands-on workshops distributed over 3 days with feedback from students after each session/day.

Criteria for assessment:

Evaluation of knowledge gained and associated to a specific task provided during the workshop will be performed during and oral assessment.

Key literature:

B. Boashash, Time-Frequency Signal Analysis and Processing, 2nd Edition, Academic Press, 2015

L. Debnath, Wavelet Transforms and Time Frequency Signal Processing, 2001

Organizer: Associate professor Romulus Lontis, lontis@hst.aau.dk

Lecturers: Associate professor Romulus Lontis, lontis@hst.aau.dk

ECTS: 1,5

Time: 21 + 28 March and 4 April 2019. All days 8:30-12:30

Place:

21 March, Fredrik Bajers Vej 7E, room 3-209

28 March and 4 April, Fredrik Bajers Vej 7E, room 3-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 28 February 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:

The course addresses advanced principles of analysis of information carried by various biological signals. Time or frequency domain analysis often fails to accurately capture the dynamics of non-stationary biological processes modulating the signals recorded by sensors. Basic principles of signal processing such as features extraction and filtering used in the time or frequency domains are extended in the joint time-frequency domain and in the joint time-scale domain. The course is intended to provide the understanding and practical use of the Wigner-Ville, Choi-Williams, Rihaczek, Cone-shaped and Adaptive-kernel quadratic time-frequency distributions, as well as of the wavelet transform with various types of wavelets. The course provides both lectures and hands-on workshops with emphasis on the practical aspects such as advantages and limitations of processing of signals recorded from the brain, muscles, and heart.

Prerequisites:

Basic knowledge in signal processing, mathematics and Matlab.

Learning objectives:

The course aims to provide the students with skills and knowledge in advanced signal processing as a valuable tool in designing of experiments and interpretation of data acquired.

Teaching methods:

Lectures and hands-on workshops distributed over 3 days with feedback from students after each session/day.

Criteria for assessment:

Evaluation of knowledge gained and associated to a specific task provided during the workshop will be performed during and oral assessment.

Key literature:

B. Boashash, Time-Frequency Signal Analysis and Processing, 2nd Edition, Academic Press, 2015

L. Debnath, Wavelet Transforms and Time Frequency Signal Processing, 2001

Organizer: Associate professor Romulus Lontis, lontis@hst.aau.dk

Lecturers: Associate professor Romulus Lontis, lontis@hst.aau.dk

ECTS: 1,5

Time: 21 + 28 March and 4 April 2019. All days 8:30-12:30

Place:

21 March, Fredrik Bajers Vej 7E, room 3-209

28 March and 4 April, Fredrik Bajers Vej 7E, room 3-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 28 February 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Eugen Romulus Lontis

Welcome to Qualitative Methods for User Research in Science, Engineering and Medicine

Description: This course will outline theory and practice of qualitative research methods within science, engineering, health, food, and medicine. A variety of methodological approaches will be introduced. There will be a special focus within video observation and ethnographic studies. Qualitative research has expanded within natural science, typically in a mix method strategy, and often with use of interviews and/or video observation. The complex interaction of users in different contexts is found in a broad range of fields in modern life and is studied in a number of different scientific fields. Qualitative research methods are important tools for approaching the understanding and interpretation of these phenomena. The course will take both a theoretical and a practical approach. The theoretical approach by relevant theories and methods for conceptualizing the design, data collection, data analysis and reporting. The practical approach by some hands-on within video observation and data analysis.

Learning objectives: The course will provide knowledge within new methods and the underlying theories such as a general understanding of interviews, video-ethnographic methods, probes, self-provided photo safaris, customer journey, interactive video footage sessions, card sorting, projective techniques, ethical considerations, data analysis with use of software. The participants will be given skills and competences (understanding, applied and able to analyze) relevant qualitative research methods, both in general and linked to own current project.

Teaching methods:

Lectures with presentation of different methodological overviews (60 %) and workshop where participants will work in groups e.g. with using video observational methods (40%).

Criteria for assessment:

l. Participation all three days

2. Optional hand-in of paper. Individual paper can be sent by e-mail after the course. Papers will be given individual feedback by course organizers. If paper hand-in 4 ECTS is given. If no paper 3 ECTS is given.

3. Presentations linked to your current PhD project. The presentation must somehow have a focus within qualitative/mixed methods research. The focus can be within empirical data, ethical issues, theoretical or even more abstract methodological questions. The duration of the presentation must not be more than 8 minutes. Your presentation should include a specific question/problem you would like for discussion/advice.

The exam ends with pass or no-pass.

Key litterature:

o Bjørner, T. ed. (2015). Qualitative Methods for Consumer Research: The Value of the Qualitative Approach in Theory and Practice. Copenhagen: Hans Reitzels Forlag. Pp. 11-112.

Supplementary literature:

o Sarah Pink (2007): Doing Visual Ethnography, 2nd. Edition: Sage.

o Raymond Gold (1958): Roles in Sociological Field Observations. Social Forces 36(3), pp. 217-223: Oxford University Press. Online, Moodle, Course, Day 1

o Ylirisku & Buur, J. (2007): Designing with Video. Focusing the usercentred design process. Springer. Onlinge, Moodle, Course Day 1

o Derry, S.J, Edt. (2007). Guidelines for Video Research in Education: University of Chicago

Organizer: Associate Professor Thomas Bjørner, email: tbj@create.aau.dk

Lecturers: Associate Professor Thomas Bjørner (AAU), Professor Bent Egberg Mikkelsen (AAU), Lene Heiselberg (DR)

ECTS: 4 (with paper), 3 (without paper)

Time: 5-7 November, 2019

Place: A. C. Meyers Vænge 15, building A, room 2.2.040

City: Copenhagen

Zip code: 2450

Number of seats: 25

Deadline: 15 October, 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: This course will outline theory and practice of qualitative research methods within science, engineering, health, food, and medicine. A variety of methodological approaches will be introduced. There will be a special focus within video observation and ethnographic studies. Qualitative research has expanded within natural science, typically in a mix method strategy, and often with use of interviews and/or video observation. The complex interaction of users in different contexts is found in a broad range of fields in modern life and is studied in a number of different scientific fields. Qualitative research methods are important tools for approaching the understanding and interpretation of these phenomena. The course will take both a theoretical and a practical approach. The theoretical approach by relevant theories and methods for conceptualizing the design, data collection, data analysis and reporting. The practical approach by some hands-on within video observation and data analysis.

Learning objectives: The course will provide knowledge within new methods and the underlying theories such as a general understanding of interviews, video-ethnographic methods, probes, self-provided photo safaris, customer journey, interactive video footage sessions, card sorting, projective techniques, ethical considerations, data analysis with use of software. The participants will be given skills and competences (understanding, applied and able to analyze) relevant qualitative research methods, both in general and linked to own current project.

Teaching methods:

Lectures with presentation of different methodological overviews (60 %) and workshop where participants will work in groups e.g. with using video observational methods (40%).

Criteria for assessment:

l. Participation all three days

2. Optional hand-in of paper. Individual paper can be sent by e-mail after the course. Papers will be given individual feedback by course organizers. If paper hand-in 4 ECTS is given. If no paper 3 ECTS is given.

3. Presentations linked to your current PhD project. The presentation must somehow have a focus within qualitative/mixed methods research. The focus can be within empirical data, ethical issues, theoretical or even more abstract methodological questions. The duration of the presentation must not be more than 8 minutes. Your presentation should include a specific question/problem you would like for discussion/advice.

The exam ends with pass or no-pass.

Key litterature:

o Bjørner, T. ed. (2015). Qualitative Methods for Consumer Research: The Value of the Qualitative Approach in Theory and Practice. Copenhagen: Hans Reitzels Forlag. Pp. 11-112.

Supplementary literature:

o Sarah Pink (2007): Doing Visual Ethnography, 2nd. Edition: Sage.

o Raymond Gold (1958): Roles in Sociological Field Observations. Social Forces 36(3), pp. 217-223: Oxford University Press. Online, Moodle, Course, Day 1

o Ylirisku & Buur, J. (2007): Designing with Video. Focusing the usercentred design process. Springer. Onlinge, Moodle, Course Day 1

o Derry, S.J, Edt. (2007). Guidelines for Video Research in Education: University of Chicago

Organizer: Associate Professor Thomas Bjørner, email: tbj@create.aau.dk

Lecturers: Associate Professor Thomas Bjørner (AAU), Professor Bent Egberg Mikkelsen (AAU), Lene Heiselberg (DR)

ECTS: 4 (with paper), 3 (without paper)

Time: 5-7 November, 2019

Place: A. C. Meyers Vænge 15, building A, room 2.2.040

City: Copenhagen

Zip code: 2450

Number of seats: 25

Deadline: 15 October, 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Thomas Bjørner

Welcome to Advanced experimental Techniques - Thermal analysis with Differential Scanning Calorimetry (DSC), Thermal Gravimetry (TG) and Dilatometry

Description:

This course is relevant for researchers form several disciplines within Engineering and Science; e.g. physics, chemistry, biology, manufacturing and mechanical engineering working with materials or substances in solid or liquid state. A theoretical background together with a practical approach on several experimental techniques used for characterizing substances is given. The course will deal with thermal analysis with focus on Differential Scanning Calorimetry (DSC), Thermal Gravimetry (TG) and dilatometry.

Thermal analysis treats changes in materials behavior in response to temperature alteration or chemical and physical processes (reactions and relaxation). With the techniques introduced in this course the researcher will be able to get information on heat capacity, enthalpy and temperature of phase changes or chemical reaction and in many cases kinetics of chemical reaction.

The course will start with chemical thermodynamics and the theoretical background for differential scanning calorimetry. Different techniques and apparatus for obtaining thermal properties will be presented; this will include power compensated DSC, heat flux DSC, DTA and modulated DSC. Topics will include: Calibration and sample preparation. Analysis of first order and second order transitions. Focus will be on polymeric materials and how to determine heat of transition, degree of crystallinity and glass transition temperature. Different techniques for determining specific heat capacity.

Prerequisites:

• Basic knowledge of materials and their thermal properties.

Learning objectives:

• Knowledge on thermodynamics on phase transformations.

• Competences in how to perform the measurement in order to retrieve the desired data.

• Competences in analysis of the provided data in order to retrieve the quantitative data for phase transformation and also chemical reactions in some cases.

Teaching methods:

Lectures and hand-on exercises with DSC, thermal gravimetry (TG) and Thermal Mechanical Analysis (TMA) both on prepared samples and samples from the students own work. The students will write a report on the performed exercises, data analysis and the obtained results.

Criteria for assessment: The assessment will be based upon the written report.

Organizer: Associate Professor Raino Mikael Larsen, rml@m-tech.aau.dk and Associate Professor Jonny Jakobsen, joj@m-tech.aau.dk

Lecturers: Raino Mikael Larsen, Johnny Jacobsen and Els Verdonck (TA Instruments)

ECTS: 4

Time: 20 - 24 May, 2019

Place:

20, 22, 23 and 24 May: Fibigerstræde 14, room 58.

21 May: Fibigerstræde 13, room 053.

Zip code: 9220

City: Aalborg Øst

Number of seats: 16

Deadline: 29 April, 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

• Teacher: Johnny Jakobsen

• Teacher: Raino Mikael Larsen

Description:

This course is relevant for researchers form several disciplines within Engineering and Science; e.g. physics, chemistry, biology, manufacturing and mechanical engineering working with materials or substances in solid or liquid state. A theoretical background together with a practical approach on several experimental techniques used for characterizing substances is given. The course will deal with thermal analysis with focus on Differential Scanning Calorimetry (DSC), Thermal Gravimetry (TG) and dilatometry.

Thermal analysis treats changes in materials behavior in response to temperature alteration or chemical and physical processes (reactions and relaxation). With the techniques introduced in this course the researcher will be able to get information on heat capacity, enthalpy and temperature of phase changes or chemical reaction and in many cases kinetics of chemical reaction.

The course will start with chemical thermodynamics and the theoretical background for differential scanning calorimetry. Different techniques and apparatus for obtaining thermal properties will be presented; this will include power compensated DSC, heat flux DSC, DTA and modulated DSC. Topics will include: Calibration and sample preparation. Analysis of first order and second order transitions. Focus will be on polymeric materials and how to determine heat of transition, degree of crystallinity and glass transition temperature. Different techniques for determining specific heat capacity.

Prerequisites:

• Basic knowledge of materials and their thermal properties.

Learning objectives:

• Knowledge on thermodynamics on phase transformations.

• Competences in how to perform the measurement in order to retrieve the desired data.

• Competences in analysis of the provided data in order to retrieve the quantitative data for phase transformation and also chemical reactions in some cases.

Teaching methods:

Lectures and hand-on exercises with DSC, thermal gravimetry (TG) and Thermal Mechanical Analysis (TMA) both on prepared samples and samples from the students own work. The students will write a report on the performed exercises, data analysis and the obtained results.

Criteria for assessment: The assessment will be based upon the written report.

Organizer: Associate Professor Raino Mikael Larsen, rml@m-tech.aau.dk and Associate Professor Jonny Jakobsen, joj@m-tech.aau.dk

Lecturers: Raino Mikael Larsen, Johnny Jacobsen and Els Verdonck (TA Instruments)

ECTS: 4

Time: 20 - 24 May, 2019

Place:

20, 22, 23 and 24 May: Fibigerstræde 14, room 58.

21 May: Fibigerstræde 13, room 053.

Zip code: 9220

City: Aalborg Øst

Number of seats: 16

Deadline: 29 April, 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

• Teacher: Johnny Jakobsen

• Teacher: Raino Mikael Larsen

- Teacher: Johnny Jakobsen
- Teacher: Raino Mikael Larsen

Welcome to Deep Learning

Description:

Deep learning is a newly emerged area of research in machine learning and has recently shown huge success in a variety of areas. The impact on many applications is revolutionary, which ignites intensive studies of this topic.

During the past few decades, the prevalent machine learning methods, including support vector machines, conditional random fields, hidden Markov models, and one-hidden-layer multi-layer perceptron, have found a broad range of applications. While being effective in solving simple or well-constrained problems, these methods have one drawback in common, namely they all have shallow architectures. They in general have no more than one or two layers of nonlinear feature transformations, which limits their performance on many real-world applications.

On the contrary, the human brain and its cognitive process, being far more complicated, have deep architectures that are organized into many hierarchical layers. The information gets more abstract while going up along the hierarchy. Interests in using deep architectures were reignited in 2006 when a deep belief network was shown to be trained well. Since then deep learning methods and applications have witnessed unprecedented success.

This course will give an introduction to deep learning both by presenting valuable methods and by addressing specific applications. This course covers both theory and practices for deep learning. Topics will include:

•Machine learning fundamentals

•Deep learning concepts

•Deep learning methods including deep autoencoders, deep neural networks, recurrent neural networks, long short-term memory recurrent networks, convolutional neural networks, and generative adversarial networks.

•Selected applications of deep learning

Literature:

Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016.

Li Deng and Dong Yu, Deep Learning: Methods and Applications, Now publishing, 2014.

Prerequisites: Basic probability and statistics theory, linear algebra and machine learning.

Organizer: Professor Zheng-Hua Tan, zt@es.aau.dk

Lecturers: Zheng-Hua Tan, Professor, Aalborg University, Denmark, zt@es.aau.dk

ECTS: 1

Time: 8-9 May 2019

8 May, 13:00-16:00

9 May, 9:00-16:00

Place: Fredrik Bajers Vej 7B, room 2-104

City: 9220 Aalborg

Number of seats: 50

Deadline: 17 April 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:

Deep learning is a newly emerged area of research in machine learning and has recently shown huge success in a variety of areas. The impact on many applications is revolutionary, which ignites intensive studies of this topic.

During the past few decades, the prevalent machine learning methods, including support vector machines, conditional random fields, hidden Markov models, and one-hidden-layer multi-layer perceptron, have found a broad range of applications. While being effective in solving simple or well-constrained problems, these methods have one drawback in common, namely they all have shallow architectures. They in general have no more than one or two layers of nonlinear feature transformations, which limits their performance on many real-world applications.

On the contrary, the human brain and its cognitive process, being far more complicated, have deep architectures that are organized into many hierarchical layers. The information gets more abstract while going up along the hierarchy. Interests in using deep architectures were reignited in 2006 when a deep belief network was shown to be trained well. Since then deep learning methods and applications have witnessed unprecedented success.

This course will give an introduction to deep learning both by presenting valuable methods and by addressing specific applications. This course covers both theory and practices for deep learning. Topics will include:

•Machine learning fundamentals

•Deep learning concepts

•Deep learning methods including deep autoencoders, deep neural networks, recurrent neural networks, long short-term memory recurrent networks, convolutional neural networks, and generative adversarial networks.

•Selected applications of deep learning

Literature:

Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, The MIT Press, 2016.

Li Deng and Dong Yu, Deep Learning: Methods and Applications, Now publishing, 2014.

Prerequisites: Basic probability and statistics theory, linear algebra and machine learning.

Organizer: Professor Zheng-Hua Tan, zt@es.aau.dk

Lecturers: Zheng-Hua Tan, Professor, Aalborg University, Denmark, zt@es.aau.dk

ECTS: 1

Time: 8-9 May 2019

8 May, 13:00-16:00

9 May, 9:00-16:00

Place: Fredrik Bajers Vej 7B, room 2-104

City: 9220 Aalborg

Number of seats: 50

Deadline: 17 April 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Zheng-Hua Tan

Welcome to Biostatistics II

Description:

This course on biostatistics will focus on the complexity of data collected in biomedical research. Therefore, the course will focus on topics like sample size estimation, meta-analysis and multi-factorial methods. This course will consist of two parts: (i) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies, and (ii) a practical part where the focus is on applying the methods to relevant and realistic data sets collected from medical and biomedical research.

The aim of the Biostatistics 2 is that you after the course will have written a full statistical analysis plan, analysed at least part of your data and written a preliminary result section and made a do-file in Stata (or another statistical software) for documentation of any statistical procedures that you have used. If you do not have any data to analyze, you should contact course leader, Carsten Dahl Mørch asap and describe the type of data you expect to have in your Ph.D and we will find a dataset for you to analyze. A short description of what should be contained in the SAP can be found in the attached files.

The learning goals for Biostatistics 2 are:

1: Write a full statistical analysis plan and understand how you can implement it to analyse your data

2: The ability to understand the assumptions and perform the following statistical tests:

Multifactorial ANOVA

Repeated measures ANOVA

Multiple and non-linear regression

Survival Analysis

3: Understand power and sample-size calculation (sample-size considerations), and perform then in the context of your own studies

4: Understand and perform meta-analyses in systematic reviews.

For the evaluation of Biostatistics II, we will attempt to get as close to your projects as possible. We hope that you will be able to use some of the statistical tools from the course and use them in the context of your own project. You should hand in the SAP in due time to allow for one of the three teachers and some of your fellow students to read and provide you with questions and feedback. You will also read and evaluate SAPs form some of your fellow students. You will present your SAP on the last day of the course.

Literature: Selected papers and book chapters will be announced to the participants shortly before the course.

Prerequisites: Biostatistics I or similar knowledge on biostatistics

Evaluation: Evaluation of the course will be based on written reports of selected exercises.

Organizer: Associate Professor Carsten Dahl Mørch, e-mail: cdahl@hst.aau.dk

Lecturers:

ECTS: 4.5

Time: 4, 8, 11, 15, 18, 22 November and 6 December 2019, from 8:15 - 12:00

Place: Fredrik Bajers Vej 7E, room 3-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 35

Deadline: 14 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description:

This course on biostatistics will focus on the complexity of data collected in biomedical research. Therefore, the course will focus on topics like sample size estimation, meta-analysis and multi-factorial methods. This course will consist of two parts: (i) a review of some well-known and widely used parametric and non-parametric methods and discussions of basic designs of experimental studies, and (ii) a practical part where the focus is on applying the methods to relevant and realistic data sets collected from medical and biomedical research.

The aim of the Biostatistics 2 is that you after the course will have written a full statistical analysis plan, analysed at least part of your data and written a preliminary result section and made a do-file in Stata (or another statistical software) for documentation of any statistical procedures that you have used. If you do not have any data to analyze, you should contact course leader, Carsten Dahl Mørch asap and describe the type of data you expect to have in your Ph.D and we will find a dataset for you to analyze. A short description of what should be contained in the SAP can be found in the attached files.

The learning goals for Biostatistics 2 are:

1: Write a full statistical analysis plan and understand how you can implement it to analyse your data

2: The ability to understand the assumptions and perform the following statistical tests:

Multifactorial ANOVA

Repeated measures ANOVA

Multiple and non-linear regression

Survival Analysis

3: Understand power and sample-size calculation (sample-size considerations), and perform then in the context of your own studies

4: Understand and perform meta-analyses in systematic reviews.

For the evaluation of Biostatistics II, we will attempt to get as close to your projects as possible. We hope that you will be able to use some of the statistical tools from the course and use them in the context of your own project. You should hand in the SAP in due time to allow for one of the three teachers and some of your fellow students to read and provide you with questions and feedback. You will also read and evaluate SAPs form some of your fellow students. You will present your SAP on the last day of the course.

Literature: Selected papers and book chapters will be announced to the participants shortly before the course.

Prerequisites: Biostatistics I or similar knowledge on biostatistics

Evaluation: Evaluation of the course will be based on written reports of selected exercises.

Organizer: Associate Professor Carsten Dahl Mørch, e-mail: cdahl@hst.aau.dk

Lecturers:

ECTS: 4.5

Time: 4, 8, 11, 15, 18, 22 November and 6 December 2019, from 8:15 - 12:00

Place: Fredrik Bajers Vej 7E, room 3-109

Zip code: 9220

City: Aalborg Øst

Number of seats: 35

Deadline: 14 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Carsten Dahl Mørch
- Teacher: Michael Skovdal Rathleff

Welcome to Bayesian Statistics, Simulation and Software – with a View to Application Examples

Description: During the last decades, Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated from the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However, the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms, Gibbs sampling and output analysis will be discussed. Furthermore, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model. The theory will be demonstrated through different examples of applications and exercises, partly based on the software package R.

Prerequisites: The course is accessible to those new to these subjects; however, a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is expected.

Additional information and assessment: All course material and additional information is available at the course website https://asta.math.aau.dk/course/bayes/2018/. In particular note the assessment of the course through active participation and a hand-in exercise.

Frequently asked questions:

Q: If I participate in the course, can you then help me analyze a dataset that I work with as part of my ph.d. project.

A: No, I am afraid that this is not possible

Q: I would like to participate in the course, but during a part of the course period I can not be present. Is it possible to follow to course via Skype or similar?

A: No, I am afraid that this is not possible

Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that possible?

A: You will have to ask the doctoral school: aauphd@adm.aau.dk

Q: I realize that I am late for enrollment, but I would really like to participate. Is it possible.

A: You will have to ask the doctoral school: aauphd@adm.aau.dk

Organizer: Professor Jesper Møller, e-mail: jm@math.aau.dk

Lecturers: Professor Jesper Møller (e-mail: jm@math.aau.dk) and Associate Professor Ege Rubak (e-mail: rubak@math.aau.dk).

ECTS: 4

Time: 4, 5, 6, 9, 10 and 11 December 2019

Place: Fredrik Bajers Vej 7A, room 4-106

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 13 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: During the last decades, Bayesian statistics has gained enormous popularity as an elegant and powerful computational tool to perform statistical analysis in complex stochastic models as applied in engineering, science and medicine. Bayesian statistics offers an alternative approach to traditional data analysis by including prior knowledge about the model parameters in form of a prior distribution. Using Bayes formula the prior distribution is updated from the posterior distribution by incorporating the observed data by means of the likelihood. Subsequently statistical inference about the unknown model parameters is derived from the posterior distribution. However, the posterior distribution is often intractable due to high-dimensional complex integrals implying that approximate stochastic simulation techniques such as Markov Chain Monte Carlo (MCMC) methods become crucial. This course reviews the basics ideas behind Bayesian statistics and Markov chain Monte Carlo (MCMC) methods. Background on Markov chains will be provided and subjects such as Metropolis and Metropolis-Hastings algorithms, Gibbs sampling and output analysis will be discussed. Furthermore, graphical models will be introduced as a convenient tool to model complex dependency structures within a stochastic model. The theory will be demonstrated through different examples of applications and exercises, partly based on the software package R.

Prerequisites: The course is accessible to those new to these subjects; however, a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is expected.

Additional information and assessment: All course material and additional information is available at the course website https://asta.math.aau.dk/course/bayes/2018/. In particular note the assessment of the course through active participation and a hand-in exercise.

Frequently asked questions:

Q: If I participate in the course, can you then help me analyze a dataset that I work with as part of my ph.d. project.

A: No, I am afraid that this is not possible

Q: I would like to participate in the course, but during a part of the course period I can not be present. Is it possible to follow to course via Skype or similar?

A: No, I am afraid that this is not possible

Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that possible?

A: You will have to ask the doctoral school: aauphd@adm.aau.dk

Q: I realize that I am late for enrollment, but I would really like to participate. Is it possible.

A: You will have to ask the doctoral school: aauphd@adm.aau.dk

Organizer: Professor Jesper Møller, e-mail: jm@math.aau.dk

Lecturers: Professor Jesper Møller (e-mail: jm@math.aau.dk) and Associate Professor Ege Rubak (e-mail: rubak@math.aau.dk).

ECTS: 4

Time: 4, 5, 6, 9, 10 and 11 December 2019

Place: Fredrik Bajers Vej 7A, room 4-106

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 13 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Jesper Møller
- Teacher: Ege Rubak

Welcome to Advanced Mathematics for PhD Candidates

Description: The aim of this course is to give the participants an idea of how the mathematical vocabulary is used and the strength of using it properly. This is needed by engineering PhD's when reading papers, which will often use the common language of mathematics, and in particular when writing papers. Moreover, it is crucial, that engineers understand when a given mathematical toolbox is applicable and when it is not. As an example: In favourable cases, differential equations have unique solutions, but this is not always true, and trying to approximate a solution in such cases may lead to results which are simply wrong. The topics covered are: metric spaces, convergence, continuity, compactness, completeness. Vector Spaces and linearity. Korovkins theorem on polynomial approximations via Bernstein polynomials. The Banach Fixed Point Theorem. Existence and uniqueness results for ordinary differential equations. The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.

Organizer: Professor Morten Nielsen, e-mail: mnielsen@math.aau.dk

Lecturers: Professor Morten Nielsen and Associate Professor Lisbeth Fajstrup

ECTS: 4

Time: 2, 4, 6, 11, 13, and 16 December 2019, from 9:00-16:00

Place: Fibigerstræde 16, room 1.111

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 11 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: The aim of this course is to give the participants an idea of how the mathematical vocabulary is used and the strength of using it properly. This is needed by engineering PhD's when reading papers, which will often use the common language of mathematics, and in particular when writing papers. Moreover, it is crucial, that engineers understand when a given mathematical toolbox is applicable and when it is not. As an example: In favourable cases, differential equations have unique solutions, but this is not always true, and trying to approximate a solution in such cases may lead to results which are simply wrong. The topics covered are: metric spaces, convergence, continuity, compactness, completeness. Vector Spaces and linearity. Korovkins theorem on polynomial approximations via Bernstein polynomials. The Banach Fixed Point Theorem. Existence and uniqueness results for ordinary differential equations. The approach in the course is to stress the necessity of precise mathematical formulation, and, in particular, to give examples where the intuitive answer is not correct.

Organizer: Professor Morten Nielsen, e-mail: mnielsen@math.aau.dk

Lecturers: Professor Morten Nielsen and Associate Professor Lisbeth Fajstrup

ECTS: 4

Time: 2, 4, 6, 11, 13, and 16 December 2019, from 9:00-16:00

Place: Fibigerstræde 16, room 1.111

Zip code: 9220

City: Aalborg Øst

Number of seats: 30

Deadline: 11 November 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Lisbeth Fajstrup
- Teacher: Morten Nielsen

Welcome to Design and Analysis of Experiments

Description: After a short survey of basic statistical concepts such as estimation, significance tests and confidence intervals, an introduction will be given to the analysis of designed experiments, including analysis of variance and factorial designs. The course will also cover multiple and polynomial regression. The course will be accompanied by an introduction to a dedicated statistical software package (R, see more at http://www.r-project.org).

Prerequisites: The course assumes basic knowledge about mathematics and probability theory as obtained through the engineering courses at Aalborg University. Some knowledge about basic statistics, such as one sample estimation and test of hypotheses, will be desirable.

Textbook: John Lawson: Design and Analysis of Experiments with R, CRC Press, 2015.

According to Deborah Worth from CRC Press there is a special price at Factum books Aalborg for the participants at this PhD course. Here is a sales LINK to the book on Factum's homepage. The book should be available in week #37.

Evaluation:

• Active attendance in at least 9 out of 12 lectures. Lecture 0 below is not mandatory and does not count for participation in the minimum required attendance of 9 lectures.

• Hand in a statistical analysis done in the last two lectures (needs to be passed)

Organizers and lecturers: Associate Professor Esben Høg, e-mail: esben@math.aau.dk and Assistant Professor Christophe Biscio, e-mail: christophe@math.aau.dk

ECTS: 4.5

Time: 10, 11, 12, 18, 19, 25 and 26 September + 2, 3, 9, 10, 23 and 24 October 2019. All days 12:30-16:15.

Place:

10, 11 and 18 September, Fibigerstræde 16, room 1.101

12 and 19 September, Fibigerstræde 14, room 59

25 and 26 September + 9 October, Fibigerstræde 16, room 1.201

2, 10 and 23 October, Fibigerstræde 16, room 1.111

3 October, Fibigerstræde 14, room 58.

24 October, Fibigerstræde 16, room 1.108.

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 20 August 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: After a short survey of basic statistical concepts such as estimation, significance tests and confidence intervals, an introduction will be given to the analysis of designed experiments, including analysis of variance and factorial designs. The course will also cover multiple and polynomial regression. The course will be accompanied by an introduction to a dedicated statistical software package (R, see more at http://www.r-project.org).

Prerequisites: The course assumes basic knowledge about mathematics and probability theory as obtained through the engineering courses at Aalborg University. Some knowledge about basic statistics, such as one sample estimation and test of hypotheses, will be desirable.

Textbook: John Lawson: Design and Analysis of Experiments with R, CRC Press, 2015.

According to Deborah Worth from CRC Press there is a special price at Factum books Aalborg for the participants at this PhD course. Here is a sales LINK to the book on Factum's homepage. The book should be available in week #37.

Evaluation:

• Active attendance in at least 9 out of 12 lectures. Lecture 0 below is not mandatory and does not count for participation in the minimum required attendance of 9 lectures.

• Hand in a statistical analysis done in the last two lectures (needs to be passed)

Organizers and lecturers: Associate Professor Esben Høg, e-mail: esben@math.aau.dk and Assistant Professor Christophe Biscio, e-mail: christophe@math.aau.dk

ECTS: 4.5

Time: 10, 11, 12, 18, 19, 25 and 26 September + 2, 3, 9, 10, 23 and 24 October 2019. All days 12:30-16:15.

Place:

10, 11 and 18 September, Fibigerstræde 16, room 1.101

12 and 19 September, Fibigerstræde 14, room 59

25 and 26 September + 9 October, Fibigerstræde 16, room 1.201

2, 10 and 23 October, Fibigerstræde 16, room 1.111

3 October, Fibigerstræde 14, room 58.

24 October, Fibigerstræde 16, room 1.108.

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 20 August 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Christophe Biscio
- Teacher: Esben Høg

Welcome to Nonlinear Differential Equations and Dynamical Systems

Description: Models for physical systems and engineering designs are often formulated in the mathematical language of systems of differential equations. To analyse the behaviour of such a system, a basic knowledge in the theory of dynamical systems is highly recommended. While linear algebra methods (decomposition of matrices) suffice to understand and even to solve systems of linear differential equations, it is in general impossible to find formulas for the solutions of nonlinear differential equations. Instead, one uses geometry based methods to obtain qualitative information about the behaviour of the solutions. Some of the catch words are: Critical points, equilibria, periodicity, invariant sets and manifolds, stability theory (Lyapunov and Poincaré), perturbations and bifurcations, chaos and attractors.

The course will be based on lectures, exercises, and on computer experiments in computer algebra systems like MAPLE (or interactive web-based solvers). It is addressed to PhD-students interested in physical modelling and stability questions related to dynamical processes. It should be of interest to students within control theory, medical electronics, signal processing, mechanical and civil engineering, physics and mathematics.

Prerequisites: A basic knowledge of mathematics, as obtained through engineering studies at Aalborg University.

Organizer: Associate Professor Lisbeth Fajstrup, email: fajstrup@math.aau.dk

Lecturers: Associate Professor Lisbeth Fajstrup, Professor Martin Raussen and Professor Rafael Wisniewski

ECTS: 3

Time: 28 and 30 October + 1, 5 and 7 November 2019

Place:

28 and 30 October + 1 and 5 November, Fibigerstræde 14, room 58

7 November, Fibigerstræde 14, room 59

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 7 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Teacher: Lisbeth Fajstrup

Teacher: Martin Hubert Raussen

Teacher: Rafal Wisniewski

Description: Models for physical systems and engineering designs are often formulated in the mathematical language of systems of differential equations. To analyse the behaviour of such a system, a basic knowledge in the theory of dynamical systems is highly recommended. While linear algebra methods (decomposition of matrices) suffice to understand and even to solve systems of linear differential equations, it is in general impossible to find formulas for the solutions of nonlinear differential equations. Instead, one uses geometry based methods to obtain qualitative information about the behaviour of the solutions. Some of the catch words are: Critical points, equilibria, periodicity, invariant sets and manifolds, stability theory (Lyapunov and Poincaré), perturbations and bifurcations, chaos and attractors.

The course will be based on lectures, exercises, and on computer experiments in computer algebra systems like MAPLE (or interactive web-based solvers). It is addressed to PhD-students interested in physical modelling and stability questions related to dynamical processes. It should be of interest to students within control theory, medical electronics, signal processing, mechanical and civil engineering, physics and mathematics.

Prerequisites: A basic knowledge of mathematics, as obtained through engineering studies at Aalborg University.

Organizer: Associate Professor Lisbeth Fajstrup, email: fajstrup@math.aau.dk

Lecturers: Associate Professor Lisbeth Fajstrup, Professor Martin Raussen and Professor Rafael Wisniewski

ECTS: 3

Time: 28 and 30 October + 1, 5 and 7 November 2019

Place:

28 and 30 October + 1 and 5 November, Fibigerstræde 14, room 58

7 November, Fibigerstræde 14, room 59

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 7 October 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Teacher: Lisbeth Fajstrup

Teacher: Martin Hubert Raussen

Teacher: Rafal Wisniewski

- Teacher: Lisbeth Fajstrup
- Teacher: Martin Raussen
- Teacher: Rafal Wisniewski

Welcome to Mixed Models with Biomedical and Engineering Applications

Description: Mixed models provide a flexible framework for analyzing data with multiple sources of random variation and they are indispensable in many medical, biological, and engineering applications. When treatments are tested in medical applications, the responses for individuals receiving the same treatment often vary due to unobserved genetic factors and this variation must be taken into account when comparing ifferent treatments. Similarly, in agricultural field trials, random soil variation affects the yield within plots. In quality control applications, the variability of the output of a production process may, apart from random noise, e.g. depend on the batches of raw material used and the employee involved in the manufacturing process. The course will provide an introduction to statistical analysis with linear mixed models. Linear mixed models is a unified framework for classical random effects ANOVA models, random coefficient models and linear models for longitudinal data with associated user-friendly implementations in R and SPSS. Linear mixed models moreover provide generalizations of the classical models to complex data not covered by he standard statistical toolbox. The course will focus on modeling with mixed models, on how a statistical analysis can be carried out for a mixed model, and on interpretation of models and results. Hands-on experience with real data will be obtained through computer exercises. Prerequisites: A basic knowledge of statistics (linear regression) and probability theory (random variables, expectation variance and covariance) is expected.

Organizer and lecturer: Professor Rasmus Waagepetersen, e-mail: rw@math.aau.dk

ECTS: 1.5

Time: 1 and 8 October 2019, from 8:15 - 16:00

Place: Fibigerstræde 14, room 59

Zip code: 9220

City: Aalborg Øst

Number of seats: 20

Deadline: 17 September 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Description: Mixed models provide a flexible framework for analyzing data with multiple sources of random variation and they are indispensable in many medical, biological, and engineering applications. When treatments are tested in medical applications, the responses for individuals receiving the same treatment often vary due to unobserved genetic factors and this variation must be taken into account when comparing ifferent treatments. Similarly, in agricultural field trials, random soil variation affects the yield within plots. In quality control applications, the variability of the output of a production process may, apart from random noise, e.g. depend on the batches of raw material used and the employee involved in the manufacturing process. The course will provide an introduction to statistical analysis with linear mixed models. Linear mixed models is a unified framework for classical random effects ANOVA models, random coefficient models and linear models for longitudinal data with associated user-friendly implementations in R and SPSS. Linear mixed models moreover provide generalizations of the classical models to complex data not covered by he standard statistical toolbox. The course will focus on modeling with mixed models, on how a statistical analysis can be carried out for a mixed model, and on interpretation of models and results. Hands-on experience with real data will be obtained through computer exercises. Prerequisites: A basic knowledge of statistics (linear regression) and probability theory (random variables, expectation variance and covariance) is expected.

Organizer and lecturer: Professor Rasmus Waagepetersen, e-mail: rw@math.aau.dk

ECTS: 1.5

Time: 1 and 8 October 2019, from 8:15 - 16:00

Place: Fibigerstræde 14, room 59

Zip code: 9220

City: Aalborg Øst

Number of seats: 20

Deadline: 17 September 2019

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

- Teacher: Rasmus Plenge Waagepetersen