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Mixed Models with Biomedical and Engineering Applications (2024)

Welcome to Mixed Models with Biomedical and Engineering Applications (2024)

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: Professor Rasmus Waagepetersen - rw@math.aau.dk

Lecturers: Professor Rasmus Waagepetersen - rw@math.aau.dk

ECTS: 1.5

Time: 16, 18 and 23 October 2024

Place: Aalborg University
16 October: Pontoppidanstræde 101 room 1.011
18 October: Pontoppidanstræde 101 room 1.011
23 October: Online (second session online for those who could not attend the 18.)

Number of seats: 20

Deadline: 02 September 2024

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

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 3.000 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 four 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.

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

Teacher: Rasmus Waagepetersen

Management of Research and Development (2024)

Welcome to Management of Research and Development

Description: The course has the two-fold purpose to prepare Ph.D. students for their future jobs by providing insight in how to carry out R&D projects in an organizational context and how to manage their own projects. A graduate from an Engineering PhD Program will typically become either a member or a manager of teams or departments working with innovation and research/development tasks. Not only will she/he be expected to be able to contribute scientifically and technically; but will also be responsible for initiating research and development projects, for managing and working with other scientists, engineers and technicians, and for establishing a constructive co-operation with other departments in the organization. This course aims at preparing the PhD students for this situation. The course will teach theory and practical methods for development proficiency in these subject-areas.

Organizer: Professor Frank Gertsen - fgertsen@mp.aau.dk

Lecturers: Frank Gertsen, Professor of Innovation Management, Dpt of Materials and Production, e-mail: fgertsen@mp.aau.dk

ECTS: 2,5

Time: 23 and 24 May 2024

Place:

Fibigerstræde 16 room 1.111

Zip code:

City:

Number of seats:

Deadline:

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 3.000 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 four 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.

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

For inquiries regarding registration, cancellation, or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Frank Gertsen

Signal and Spectral Analysis: extracting information from noisy data (2024)

Welcome to Signal and Spectral Analysis: extracting information from noisy data (2024)

Description:
In many situations, a number of observations are made which contain some information about an underlying phenomenon we are interested in. Examples of this are:

The diagnosis of the Parkinson’s disease from a telephone recording,
The assessment of bearing wear from vibrational data,
The automatic transcription of music,
Order tracking analysis of rotating machines,
Automated analysis of the heart sound, and
Harmonic analysis in power systems.

To solve these and many other problems, a signal analysis toolbox is needed. This course focuses ondeveloping, explaining, understanding, and using such tools. Specifically, the course covers important and general concepts such as:

Signal modelling: Which models exist and what are their applicability and limitations?
Spectral analysis: Why is signal analysis often performed as a function of frequency and how do you do it?
Inference and parameter estimation: How do you estimate model parameters accurately and quantify how well you do?

The course is primarily developed for doctoral students from medicine and various engineering and natural science disciplines who wish to not only apply, but also to understand signal and spectral analysis. Consequently, the course is rooted in a principled and systematic exposition of fundamental concepts and tools and in a scientific approach which promotes the creation of knowledge over improving state-of-the-art by ε percent. An important goal of the course is to make doctoral students able to solve a signal and spectral analysis task based on data from their own Ph.D.-project. This is integrated in the course via a mini project.

Keywords: Filtering, statistical signal processing, estimation theory, maximum likelihood, powerspectral density estimation, modelling, least squares, autoregressive, nonnegative matrix factorizations, sparsity, periodic signals, Fourier analysis, line spectra.

Prerequisites: Basic probability theory, linear algebra, signal processing, and experience with MATLAB and/or Python programming.

Organizer:

Assoc.

Lecturers:

Assoc. Professor Jesper Rindom Jensen - jrj@es.aau.dk

ECTS:

3.0

Time:

Place:

Aalborg University,

Fredrik Bajers Vej 7C room 3-204

Zip code:

City:

Teams Channel:

LINK

Number of seats:

Deadline:

Important information concerning PhD courses:

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Mads Græsbøll Christensen
Teacher: Jesper Rindom Jensen

Stakeholder and user-involvement in technological innovation and implementation (2024)

Welcome to Stakeholder and user-involvement in technological innovation and implementation: Theoretical and practical aspects (2024)

Description:
Many Ph.D. students in science, engineering and health need to involve and engage with stakeholders and technology users during their research. This course presents both selected theories on the Human – Technology interface, and methods for facilitating and generating qualitative data on user-involvement in technological innovation and implementation.

Pre-requisite: MSc degree.

Learning objectives:

Identify methodological and theoretical dilemmas and challenges regarding stakeholder and user-involvement in technological innovation and implementation
Make qualified judgments regarding stakeholder and user-involvement in technological innovation and implementation
Make strategies to handle conflicting interests research projects that involve stakeholders and users
Choose theories and methods relevant for research applying stakeholder and user-involvement in technological innovation and implementation
Present challenges and dilemmas in own Ph.D. project that regard stakeholder and user-involvement in technological innovation and implementation

Teaching methods:

The course is designed so that the two first days are split up into two sessions: One before lunch and one after. Usually a session begins with a lecture (90 minutes, including breaks) followed by discussions or group work.

In preparation to the fourth day participants are kindly asked to do a ppt presentation on dilemmas and challenges in their Ph.D. project regarding stakeholder and user-involvement in technological innovation and implementation (duration: no more than 20 minutes). The third day is allocated to preparation of the presentation, and we will use the group work and discussions during the first two days to qualify the presentations. Course participants are asked to begin reflecting on stakeholder and user issues in own project when reading the course material.

Criteria for assessment:
Reading the text material connected to the lectures, and preparing a ppt presentation on dilemmas and challenges of stakeholder involvement in your Ph.D. project, are mandatory activities for all participants.

Program:

Day 1, morning: Socio-technical understandings of technology. Lecturer: TBA [AALBORG CAMPUS]
Day 1, afternoon: Citizen Science. Lecturer: Eglė Butkevičienė [AALBORG CAMPUS]
Day 2, morning: Action research. Lecturer: Tom Børsen [AALBORG CAMPUS]
Day 2, afternoon: Participatory research. Lecturer: Petko Karadechev [AALBORG CAMPUS]
Day 3, PhD students prepare their presentation for day 4 [AT HOME]
Day 4, PhD students' presentations in parallel sessions [ONLINE]. 1 hour per student.

Key literature:

Selected chapters in:

Børsen, T., & Botin, L. (2013). What is techno-anthropology? In T. Børsen, & L. Botin (Eds.), What is techno-anthropology? (pp. 7-31). Aalborg: Aalborg University Press.

Botin, L., Bertelsen, P., & Nøhr, C. (2015). Techno-anthropology in health informatics: Methodologies for improving human-technology relations. Amsterdam, Berlin, Tokyo, Washington: IOS Press.

Kanstrup, A. M., & Bertelsen, P. (2011). User innovation management: A handbook Aalborg Universitetsforlag.

Kanstrup, A. M., Bygholm, A., & Bertelsen, P. (2017). Participatory design & health information technology IOS Press.

Organizer: Associate Professor Tom Børsen - boersen@plan.aau.dk

Lecturers: Full Professor Eglė Butkevičienė, Associate Professor Tom Børsen, Assistant Professor Petko Karadechev.

ECTS: 2.5

Time: 20, 21, 22, 23 August 2024

Place: Rendsburggade 14 room, floor, 4 room 4.517

Zip code: 9000

City: Aalborg

Number of seats: 30

Deadline: 09 August 2024

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 3.000 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 four 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.

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

For inquiries regarding registration, cancellation, or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Project Management and Interpersonal Skills -A (2024)

The course is fully booked! Please sign up at the waiting list.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk.

Welcome to Project Management and Interpersonal Skills - A (2024)

Description:

Research, long term experience as PhD coach and feed back from earlier participants at this course show that PhD students benefit immensely from being challenged to reflect on, discuss and experiment with their planning practice and the way they approach cooperation with supervisors and other important stakeholders.

The workshop will introduce and illustrate a method on how to plan your time on a daily and long term basis inspired by Steven Covey (2005) and the game plan approach from the world of coaching. The course will also work with the negotiation and conflict management approach from the Harvard Negotiation project (Stone, Patton and Heen, 2010) as a tool to explore interpersonal communication structure. Key issues in the relationship between PhD student and supervisor will be identified and analyzed by means of practical exercises.

There will be a possibility for an individual follow up coaching session after a month for those PhD students that find it beneficial.

Target group:

• PhD students preferably during their first year

Time schedule for the workshop: One workshop will be held in the spring and one in the fall

Understand the challenges of project planning as a PhD student
Reflect on ones own planning practice and the specific pittfalls and resources one poses
Develop strategies to address those pittfalls and provide feedback to other PhD students
Practice a more realistic and efficient planning
Understand the individual needs and challenges in specific cooperations with supervisors and other important stakeholders
Apply important negotiating skills

Course structure:

2 consecutive days consisting of a mix of introductions and exercises individually and in groups.
Two assignments related to actual skills is to be send in before the workshop

Completion of the course: Handing in assignments and participating in the course are criteria for passing the course.

Course program: A detailed program will be send by email upon acceptance and in due time for preparations.

Course literature (Primary in bold):
Covey, S.R. (2005). The seven habits of highly effective people. ISBN 9780743268165

Conflict resolution. A brief introduction (No date, no author)

Patton, B. (2017). You can’t win by avoiding difficult conversations in Journal of Business & Industrial Marketing 3274, 553-557. Emerald Publishing Limited. ISSN 0885-8624

Heim, C. (2012). Tutorial facilitation in the humanities based on the tenets of Carl Rogers in Higher Education 63: 289-298. Springer. DOI 10.1007/s10734-011-9441-z

Prochaska, J. O. , Norcross, J.C and Diclemente, C.C. (2007). Changing for good. A Revolutionary Six-Stage Program for Overcoming Bad Habits and Moving Your Life Positively Forward. HarperCollins Publishers, New York, USA.

Organizer:

Lecturers:

ECTS:

Time:

07 and 08 May 2024, both days from 9-16

Place:

Rendsburggade 6 room 271

Zip code:

9000

City:

Aalborg

Number of seats:

20

Deadline:

16 April 2024

NB This course is only for PhD Students at Aalborg University

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 3.000 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 four 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.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Pia Bøgelund
Teacher: Henrik Worm Routhe

Project Management and Interpersonal Skills -B (2024)

The course is fully booked! Please sign up at the waiting list.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk.

Welcome to Project Management and Interpersonel Skills - B (2024)

Description:

Research, long term experience as PhD coach and feed back from earlier participants at this course show that PhD students benefit immensely from being challenged to reflect on, discuss and experiment with their planning practice and the way they approach cooperation with supervisors and other important stakeholders.

The workshop will introduce and illustrate a method on how to plan your time on a daily and long term basis inspired by Steven Covey (2005) and the game plan approach from the world of coaching. The course will also work with the negotiation and conflict management approach from the Harvard Negotiation project (Stone, Patton and Heen, 2010) as a tool to explore interpersonal communication structure. Key issues in the relationship between PhD student and supervisor will be identified and analyzed by means of practical exercises.

There will be a possibility for an individual follow up coaching session after a month for those PhD students that find it beneficial.

Target group:

• PhD students preferably during their first year

Time schedule for the workshop: One workshop will be held in the spring and one in the fall

After the course the PhD student should be able to:

Understand the challenges of project planning as a PhD student
Reflect on ones own planning practice and the specific pittfalls and resources one poses
Develop strategies to address those pittfalls and provide feedback to other PhD students
Practice a more realistic and efficient planning
Understand the individual needs and challenges in specific cooperations with supervisors and other important stakeholders
Apply important negotiating skills

Course structure:

2 consecutive days consisting of a mix of introductions and exercises individually and in groups.
Two assignments related to actual skills is to be send in before the workshop

Completion of the course: Handing in assignments and participating in the course are criteria for passing the course.

Course program: A detailed program will be send by email upon acceptance and in due time for preparations.

Course literature (Primary in bold):
Covey, S.R. (2005). The seven habits of highly effective people. ISBN 9780743268165

Stone, D., Patton, B. and Heen, S. (2010). Difficult conversations. How to discuss what matters most. Penguin Books. London, England.

Patton, B. (2017). You can’t win by avoiding difficult conversations in Journal of Business & Industrial Marketing 3274, 553-557. Emerald Publishing Limited. ISSN 0885-8624

Heim, C. (2012). Tutorial facilitation in the humanities based on the tenets of Carl Rogers in Higher Education 63: 289-298. Springer. DOI 10.1007/s10734-011-9441-z

Prochaska, J. O. , Norcross, J.C and Diclemente, C.C. (2007). Changing for good. A Revolutionary Six-Stage Program for Overcoming Bad Habits and Moving Your Life Positively Forward. HarperCollins Publishers, New York, USA.

Organizer:

Lecturers:

ECTS:

Time:

Place:

Rendsburggade 14 room 4.231

Zip code:

City:

Number of seats:

Deadline

:

Important information concerning PhD courses:

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Pia Bøgelund
Teacher: Henrik Worm Routhe

Data Science Using R (2024)

Welcome to Data Science using R (2024)

Description:

Most quantitative research projects involve case-specific programming tasks as well as data analysis of a more standard nature. When working with quantitative data it is essential to be able to do so in a systematic and reproducible manner with trustworthy software. This course, Data Science using R, 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. Topics of the course will include:

Introducing R as a statistical programming environment for data analysis.
Efficient data management and high-level graphics using R
Statistical modelling in theory and practice using R.
Learning from large datasets using R.
Aspects of scientific computing in theory and practice.
Reproducible research in practice.
Programming in R.

Prerequisites (important):

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/.

Learning objectives:

After completing the course the participants will:

be able to solve programming tasks with R.
know how to manage data, visualize data and fit models to data using R.
have learned enough about R to continue learning on their own.

Teaching methods:

A combination of instructive videos, computer practicals and lectures.

Course organization:

The course gives 4 ECTS credits which means that the workload of the course is about 120 hours. The course is organized in three blocks each consisting of two days. The blocks are two weeks apart. Prior to the first block and between the blocks there will be tasks for participants to work on, either independently or in smaller groups. After the last block, there will be time to work on the major exercise to be handed in.

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: aauphd@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 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

Organizers: Søren Højsgaard and Ege Rubak

ECTS:

4

Time:

13, 14 March, 3, 4, 24, 25 April 2024

Place:

Aalborg University - Fredrik Bajers Vej 7C room 3-204

Zip code:

9220

City:

Aalborg

Number of seats:

40

Deadline:

21 February 2024

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

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 3.000 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.

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

Teacher: Søren Højsgaard
Teacher: Ege Rubak

Scientific Computing using Python - 1. Python + Scientific Computing (2024)

The course is fully booked! Please sign up at the waiting list.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk.

Welcome to Scientific Computing using Python - 1. Python + Scientific Computing (2024)

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 improved productivity by reusing code and software.

This is an introductory course in scientific computing using the popular programming language Python. Python is very popular 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:

The Python programming language itself and
various aspects of scientific computing.

This specific course content is as follows:

The Python language

Course introduction
1. Historical overview of scientific computing and high performance computing
Python development environment
1. Python from above
2. Data types, built-in functions
3. Branching and looping
4. Functions (definition, built-in, lambda)
5. Modules and packages
Debugging and testing
1. Pytest
2. Doctest
3. Pdb (breakpoints and post-mortem debugging)
Basic scientific computing packages
1. Pandas (tabular data - numerical computing)
2. Seaborn (data visualization) + other visualisation packages
3. H5py (data storage/access via HDF)
4. NumPy (numerical computing - array-based - vectorization) lighter overview
5. SciPy (various tools for integration, optimization, etc.) lighter overview
6. Documentation using Sphinx

Scientific computing

Basic issues related to computational sciences such as
1. Floating-point representation
2. Numerical accuracy and condition number
3. Cancellation
4. Algorithmic complexity
Scientific software development
1. Version control (via git)
2. Code documentation
3. Test procedures (what to test - and how)
4. Code refactoring

Audience: The targeted audience is all PhD students 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 if using parallel computing (but this should not be a problem for this first part of the course).

Learning objectives: After completing the course the participants will:

have fundamental knowledge of important aspects of scientific computing
be able to map a mathematically formulated algorithm to Python code
know how to document, debug and test the developed code.
know when and how to optimize Python code

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

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

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

Selection of a few chapters in Python books (specified under the individual days below)
References to Python and all relevant packages (freely available via http://python.org)
A number of scientific papers relevant for specific parts of the course (specified under the individual days below)

Organizer and lecturer: Associate Professor Jimmy Jessen Nielsen

ECTS:

Time: 12, 13, 14 June 2024

Place: Aalborg University Fredrik Bajers Vej 7C room 3-204

Zip code: 9220

City: Aalborg

Number of seats: 25 extended to 35

Deadline: 22 May 2024

NB This course is only for students at Aalborg University

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 3.000 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 four 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.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Jimmy Jessen Nielsen

Scientific Computing using Python - High Performance Computing In Python (2024)

Welcome to Scientific Computing using Python - High Performance Computing in Python (2024)

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 popular programming language Python. Python is popular 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

High-performance computing and computer architectures
Performance optimization
1. Numba (just in time compilation)
2. Numba GPU functionality
3. Other options - superficially: Cython (compiled Python via C-extensions), f2py (inclusion of Fortran code in Python)
Parallel/distributed computing
1. Theoretical aspects (Amdahl's and Gustafson-Barsis' law)
2. Parallel computing on one computer
3. Distributed computing across multiple computers

Audience: The targeted audience is all PhD students with some experience in Python programming and an interest in developing high-performance Python 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 pre-specified 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 project and at-least 75% participation is required to pass the course.

Learning objectives: After completing the course the participants will:

Know how to use methods and software for performance optimization.
know when and how to apply parallel computing for scientific computing.

Organizer and lecturer: Sokol Kosta

ECTS:

Time:

June 17-18, 2024

Place:

, Fredrik Bajers Vej 7C room 3-204

Zip code:

City:

Number of seats:

Deadline:

NB This course is only for PhD Students at Aalborg University

Important information concerning PhD courses:

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Teacher: Sokol Kosta

Machine Learning (2024)

Welcome to Machine Learning (2024)

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 decades, 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, prediction, clustering, generative modeling and anomaly detection. 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 methods: logistic regression, support vector machines, neural networks, K-nearest neighbors, decision trees, boosting
Unsupervised learning and clustering methods: K-means, Gaussian mixture models, Expectation Maximization algorithm, principal component analysis
Deep learning methods: deep neural networks, long short-term memory recurrent neural networks, convolutional neural networks, generative adversarial networks.
Probabilistic graphical models
Reinforcement learning

Prerequisites: Basic probability and statistics theory, linear algebra and basic programming skills.

Literature (finding one that interests you):

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
Machine Learning: A Bayesian and Optimization Perspective, Sergios Theodoridis, Academic Press, 2020.

Organizer:

Lecturers:

zt@es.aau.dk

sarthaky@es.aau.dk

ECTS:

Time:

Place:

Aalborg University

Fibigerstræde 10 room 09

Fredrik Bajers Vej 7A room 4-106

Zip code:

City:

Number of seats:

Deadline:

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Important information concerning PhD courses:

Teacher: Zheng-Hua Tan
Teacher: Sarthak Yadav

Introduction to Structural Equation Modelling (2024)

Welcome to Introduction to Structural Equation Modelling (2024)

Description:

Research typically address topics such as “organizational culture”, “project success”, and “trust”. These concepts are latent variables or non-observable variables that cannot be directly measured with a single metric. Instead, they are indirectly measured using a set of indicators or manifest variables. Structural Equation Modelling (SEM) is one way to analyze such nonobservable variables and the relationships between them.

SEM is a very powerful tool in a variety of scenarios, such as:
1. When the research goal is to predict key constructs or to identify key constructs.
2. When the researcher has complex models that comprise many different constructs, indicators, and relationships.
3. When the research model is evaluated with secondary or archival data.

Prerequisites:
Introduction to statistics (solid knowledge of multiple linear regressions).

Learning objectives:
The ultimate goal is to develop from a hypothesized theory a structural model and evaluate it based on the most recent assessment criteria.

Teaching methods:
Lectures, exercises, and individual case studies.

Criteria for assessment:
Written assignment of an individually developed SEM model

Organizer: Daniel Russo

Lecturers: Daniel Russo, Associate Professor at the Department of Computer Science, AAU-Copenhagen

ECTS: 4

Time: 4-6 March 2024

Place: Aalborg University
4 March - Fibigerstræde 4 room 125
5 March - Fibigerstræde 4 room 7
6 March - Fibigerstræde 10 room 09

Zip code:

City:

Number of seats:

Deadline:

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration, aauphd@adm.aau.dk

Important information concerning PhD courses:

For external PhD students: This course is a general course and is prioritized for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

Teacher: Daniel Russo