Welcome to AI for the People 

Description: The notion of Artificial Intelligence (AI) dates back approx. 70 years as a research field and even longer if one considers fiction writers. A number of different definitions of AI has been suggested over the years, but none seem to capture what AI is. This might be due to the fact that AI is about computer algorithms that behave intelligently. And since the capabilities of computer algorithms improve over time, no static definition is possible.

One aspect of AI is the ability to learn or adapt dynamically. This concept has inspired numerous Sci-fi books and movies with the underlying theme of man vs AI (often manifested in a robot). From this follows naturally ethical and regulatory considerations. But until recently, such considerations (see for example the three Robotic laws defined by the sci-fi writer I. Asimov) have been speculative since current AI algorithms (and their manifestation in mechanical devices) have performed poorly and hence never left university labs around the world. Recently, however, fast hardware and massive amount of data have allowed revisiting one particular AI algorithm invented in the 80s, namely Artificial Neural Networks (ANN), and increasing the size of the networks used in these models. This was exemplified via image processing for recognizing hand-written digits and resulted in amazing results. Inspired by this success ANN (now known as Deep Learning (DL)) was quickly picked up by other research fields where similar successes have been witnessed.

DL algorithms can now outperform humans on a number of tasks. Moreover, they can, to a certain degree, learn new tasks. An important point in this regard is that the algorithm is so complex that it is next to impossible to understand its inner workings. So, we seem to be facing a reality where AI, in a not too distant future, will be used to make decisions (simply because it is of better than humans). This raises a number of ethical and regulative questions such as, for instance, 1) how we ensure that AI systems are not discriminating against certain groups in the population, 2) how do we ensure transparency about the decisions made by AI systems, and relatedly 3) could and should individuals be given a substantial right to an explanation of decisions made by such systems and a substantial right not to be subjected to automated decision-making (GDPR). Since many of the currently developed AI systems operate on the basis of large amounts of data, the development and use of such systems also reinvigorate the ethical issues related to ‘Big data’. Finally, there are problems related to the efficacy and safety of AI systems. This raises questions not only of how appropriate monitoring of the development of these systems can be secured, but also and more importantly about the appropriate domains for use.

These questions and related questions are the core focus of the PhD course on ‘AI for the people’. The aim is to raise an awareness in the participants. To this end the course will be a combination of lectures, debates and an assignment, and includes the following topics:

  • Introduction to AI
  • Ethical issues in the development and use of AI
  • Industry perspective on AI

 

Organizers: Professor Thomas B. Moeslund, tbm@create.aau.dk

Lecturers:  Thomas B. Moeslund, Thomas Ploug and Bjørn Skou Eilertsen

ECTS: 2

Time: 26 March and 16 April 2020

Place:

City: Aalborg

Number of seats: TBD

Deadline: 5 March 2020

 

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 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.

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

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.

 

Organizers: Associate Professor Jesper Kjær Nielsen, jkn@create.aau.dk  and Professor Mads Græsbøll Christensen, mgc@create.aau.dk

Lecturer: Associate Professor Jesper Kjær Nielsen, Professor Mads Græsbøll Christensen and Associate Professor Jesper Rindom Jensen

ECTS: 3

Time: 26-30 October 2020

Place:

Zip code: 9000

City: Aalborg

Number of seats: 30

Deadline: 5 October 2020

 

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 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.

Welcome to Advanced Experimental Techniques - Infrared and Raman Spectroscopy


Description: This course introduces participants to the theory and practice of infrared and Raman spectroscopy as applied to the study of the physical and chemical characteristics of materials and substances. Its purpose is to provide a firm foundation in the subject concentrating on the fundamental principles, exploration of modern instrumentation applied in these techniques and description of a broad range of application examples from different areas of materials characterization. In order to leave room for a large number of application examples, the theoretical part does not go into more depth than it is necessary for a beginner`s understanding. 
Hands-on sessions will be organized where the participants will carry out spectroscopic experiments on material samples provided and/or samples brought by participants themselves. Preparation of the report based upon obtained results will be required after the hands-on sessions.

Organizer: Associate Professor Lars Rosgaard Jensen, lrj@mp.aau.dk

ECTS: 3 (requires active participation in lectures and exercises and submission of a final report of sufficient quality based on the performed exercises, data analysis and the obtained results)

Time: 9-13 November 2020, 09:00-15:30

Place:

City:

Number of seats: 20 (for hands-on exercises max. 15 participants can attend)

Deadline: 19 October 2020

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 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.

 

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: 30 november + 2, 4, 9, 11 and 14 December 2020

Place: 

Zip code: 
9220

City: 
Aalborg Øst

Number of seats: 30

Deadline: 9 November 2020

 

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 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.

 



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: Note that this will not be a "a black box approach" to the subject as there will be some mathematical abstraction which is needed in order to construct meaningful Bayesian models and simulation procedures. In principle the course is accessible to those new to these subjects, however, some mathematical training will be an advantage and a basic knowledge of statistics and probability theory as obtained through engineering studies at Aalborg University is definitely expected.

 

Additional information and assessment: All course material and additional information is available at the course website https://asta.math.aau.dk/course/bayes/2019/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: 25 - 27 November and 30 November - 2 December 2020

Place: 

Zip code:
 9220

City:
 Aalborg Øst

Number of seats: 40

Deadline: 4 November 2020

 

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 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.

 

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.

 

Remarks:

  • The course will use exclusively R. However, there exists other software/libraries for design and analysis of experiment such as: JMP, the Statistics and Machine Learning Toolbox in Matlab, the python library pyDOE2.
  • Most of the examples in the course come from the food industry but the methods introduced may of course be used in other fields.

 

Evaluation:

  • Active attendance in at least 9 out of 12 lecturesLecture 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: 16-17, 23-24 and 30 September + 1, 7-8, 14-15 and 21-22 October 2020

Place: 

Zip code: 9220

City: Aalborg Øst

Number of seats: 40

Deadline: 26 August 2020

 

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 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.

 



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 report including at least parts 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.

 

The learning goals for Biostatistics 2 are:

1: Write a statistical statistical preferably based 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 statistical report 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 statistical reports form some of your fellow students. You will present your statistical report 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 report and its presentation.

 

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

Lecturers: Associate Professor Carsten Dahl Mørch.

ECTS: 4.5

Time: 5, 9, 12, 16, 19 and 23 November + 7 December 2020

Place: 

Zip code: 
9220

City: 
Aalborg Øst

Number of seats: 35

Deadline: 15 October 2020

 

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 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.

 



Welcome to Advanced Energy System Analysis on the EnergyPLAN model

Description: The PhD course gives an introduction to advanced energy system analysis using the EnergyPLAN computer model. After the course the participants are expected to be able to understand methodologies of advanced energy system analysis and to be able to use the EnergyPLAN computer model as a tool in making energy system analyses.

The course is conducted as a combination of lectures and computer workshops of a total of 4 days (32 hours) and assignments of a total of 6-7 days (52 hours).  Results of assignments will be presented by the participants.

The course start with an introduction to the model (installation, using, constructing new data sets) and proceeds to focus on the use of the model in

  • sustainable cities and communities
  •  technical analyses of large-scale integration of wind.
  • analyses of exchange with external electricity markets
  • combinations of different renewable energy technologies.
  • designing flexible energy systems using flexible technologies such as heat pumps, hydrogen storage, pumped storage etc.
  • district heating systems versus individual houses and zero energy buildings
  • designing energy systems based on multiple criteria

 

Course fee: 750 DKK (100 EUR) for PhD students. Others pay 7500 DKK. All participants must cover own costs for travel and accommodation.

In order to sign up you must pay (no later than 23 April) using the following link:  https://www.erap.aau.dk/event/index.php/ESAotEPM2019

 

Organizer: Professor Henrik Lund lund@plan.aau.dk

Lecturers: Poul Alberg Østergaard, Henrik Lund, Jakob Zinck Thellufsen and & Brian Vad Mathiesen

ECTS: 3

Time: 20-22 April and 4-6 May 2020

Place:

City:

Number of seats: 25

Deadline: 30 March

 

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 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.

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 (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.


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

ECTS: 

Time: 9, 11, 13, 16 and 18 March 2028

Place: 

Zip code: 
9220

City: 
Aalborg Øst

Number of seats: 50

Deadline: 17 February 2020

 

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 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.

 



Welcome to Introduction to Image Processing and Computer Vision


Description:
The purpose of this course is to motivate doctoral students from different fields of engineering and medicine to get familiar with basic and advanced topics of image and video processing and apply them in their own PhD projects. The course will start by giving basic image processing concepts on how an image is formed, manipulated, and analyzed. Then, the course continues into more advanced topics like different imaging sensors and data, object detection, recognition, and tracking.

Prerequisites: Basic linear algebra and Matlab.

Organizer: Associate Professor Kamal Nasrollahi, e-mail: kn@create.aau.dk

Lecturers:

ECTS: 3

Place: 

City:

Time: 6 and 13 April + 11 May 2020

Number of seats: 20

Deadline: 16 March 2020

 

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 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.

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 computing on 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" and again in November. 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: TBA

Place: 

City: 9220 Aalborg Øst

Number of seats: 25 

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 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 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.

 



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

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 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:

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

 

This specific course content is as follows:

The Python language

  1. Course introduction
    1. Historical overview of scientific computing and high performance computing
  2. 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
  3. Debugging and testing
    1. Pytest
    2. Doctest
    3. Pdb (breakpoints and post-mortem debugging)
  4. Basic scientific computing packages
    1. NumPy (numerical computing - array-based - vectorization)
    2. SciPy (various tools for integration, optimization, etc.)
    3. Matplotlib (data visualization) + other visualisation packages
    4. H5py (data storage/access via HDF)
    5. Documentation using Sphinx

 

Scientific computing

  1. Basic issues related to computational sciences such as
    1. Floating-point representation
    2. Numerical accuracy and condition number
    3. Cancellation
    4. Algorithmic complexity
  2. 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 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 if 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 Jupyter notebooks, smaller exercises, and a mini-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 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: TBA


Place: 

Zip code: 
9220 

City: 
Aalborg Øst

Number of seats: 25

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 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 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.

Welcome to Datascience 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/ 

 

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: 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 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  

 

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

Lecturers: Associate Professor Torben Tvedebrink and Associate Professor Mikkel Meyer Andersen


ECTS: 4

Time: 31 March - 1 April, 21-22 and 28-29 April 2020

Place: 

City: 
9220 Aalborg Øst

Number of seats: 
40

Deadline: 
10 March 2020

 

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 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.

 

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: 29 September and 6 October 2020

Place: 

Zip code: 
9220

City: 
Aalborg Øst

Number of seats: 20

Deadline: 8 September 2020

 

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 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.