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Advanced Mathematics for PhD Candidates (2023)

Welcome to Advanced Mathematics for PhD Candidates (2023)

Description:

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Teacher: Lisbeth Fajstrup
Teacher: Morten Nielsen

Advanced Qualitative Methods for User and Consumer Research (2023)

Welcome to Advanced Qualitative Methods for User and Consumer Research

Description:

This course will outline theory and practice of qualitative research. The entire course will have a very applied approach towards qualitative studies. A variety of methodological approaches will be introduced and a special focus will be within: 1.

How to implement validity and reliability in qualitative studies. 2.

How to implement Mixed Methods. 3.

How to analyze and visualize qualitative data.

Course content: The course will present new methods and the underlying theories such as a general understanding of interviews, ethnographic methods, probes, customer journey, interactive sessions, card sorting, projective techniques, ethical considerations, data analysis with use of software. Further, there will be structured elements for improving validity and reliability within qualitative studies. The participants will work with own cases during the PhD course, and will use relevant qualitative research methods. The practical approach will be included by some hands-on interviews/ observations, and data analysis.

Format: Lectures with presentation of different methodological overviews (50%). Participant presentations with added theoretical and practical discussions (25%). Workshop where participants will work in groups e.g. with using observational methods. The groups set-up own theoretical focus (25 %).

Exam:

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, and theoretical or even more abstract methodological questions. The duration of the presentation must NOT e

xceed

10 minutes. T

here will be 20 minutes for discussion.

Your presentation should include a specific question/ problem you would like for discussion/advice.

Mandatory readings:

A: 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. Buy at Saxo, Hans Reitzel, Amazon, Factum Books or other book stores.

B: Bjørner, T., Korsgaard, D., Reinbach, H. C., & Perez-Cueto, F. J. (2018). A contextual identification of home-living older adults' positive mealtime practices: A honeycomb model as a framework for joyful aging and the importance of social factors. Appetite, 129, 125-34. https://doi.org/10.1016/j.appet.2018.07.005

C: MacPhail, C., Khoza, N., Abler, L., & Ranganathan, M. (2016). Process guidelines for establishing Intercoder Reliability in qualitative studies. Qualitative Research, 16(2), 198-212. https://doi.org/10.1177/1468794115577012

D. O’Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: a synthesis of recommendations. Academic Medicine, 89(9), 1245-1251. https://pubmed.ncbi.nlm.nih.gov/24979285/

E: Bjørner, T., & Schrøder, M. (2019). Advantages and challenges of using mobile ethnography in a hospital case study: WhatsApp as a method to identify perceptions and practices. Qualitative Research in Medicine and Healthcare, 3(2). https://doi.org/10.4081/qrmh.2019.7795

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

Lecturers: Associate Professor Thomas Bjørner (AAU),

ECTS: 4.0

Time: 8 -9 November 2023

Place: 8 November, Aalborg University Copenhagen, A.C. Meyersvænge 15, 2.1.009, 2450 Copenhagen SV.

9 November, 8:15 -12: 00 Aalborg University Copenhagen, A.C. Meyersvænge 15, 2.1.009, 2450 Copenhagen SV.

9 November, 12:00 -16:30 Aalborg University Copenhagen, A.C. Meyersvænge 15, 2.1.102 , 2450 Copenhagen SV.

Number of seats: 20

Deadline: 17 October 2023

Important information concerning PhD courses:

Teacher: Thomas Bjørner

AI for the People (2023)

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

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Rendsburggade 14, 9000 Aalborg

Room: 4.517

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Teacher: Thomas B. Moeslund
Teacher: Thomas Ploug

Bayesian Statistics, Simulation and Software – With a View to Application Examples (2023)

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/2023/. 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: Maybe, to some extend. See the course website

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 - jm@math.aau.dk

Lecturers: Professor Jesper Møller - jm@math.aau.dk; Associate Professor Ege Rubak - rubak@math.aau.dk

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Fibigerstræde 10, room 09

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Teacher: Jesper Møller
Teacher: Ege Rubak

Biostatistics II (2023)

Welcome to Biostatistics II (2023)

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 II are:

Write a statistical statistical preferably based your data
The ability to understand the assumptions and perform the following statistical tests:
Multifactorial ANOVA
Repeated measures ANOVA
Multiple and non-linear regression
Survival Analysis
Understand power and sample-size calculation (sample-size considerations), and perform then in the context of your own studies
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:

cdahl@hst.aau.dk

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Teacher: Carsten Dahl Mørch

Data Science using R (2023)

Welcome to Data Science using R (2022)

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:

02, 03, 16, 17, 30, 31 March 2023

Place:

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

Zip code:

9220

City:

Aalborg

Number of seats:

40

Deadline:

09 February 2023

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.

Teacher: Søren Højsgaard
Teacher: Ege Rubak

Deep Learning (2023)

Description:
Deep learning is the hottest 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 have been reignited by many amazing applications enabled by deep learning techniques.

During the past years, 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. The students will also have hands-on exercises experimenting a variety of deep learning architectures for applications. Topics will include:

- Machine learning fundamentals
- Deep learning concepts
- Deep learning methods including deep autoencoders, deep neural networks, long short-term memory recurrent neural networks, convolutional neural networks, and generative adversarial networks.
- Selected applications of deep learning
- Machine learning operations (MLOps)

Teaching methods: The course will be taught through a combination of lectures, demos of applications and small exercises.

Criteria for assessment: Acceptable exercise solutions and at-least 75% participation are required to pass the course.

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

Lecturers: Professor Zheng-Hua Tan, e-mail: zt@es.aau.dk

Dr. Ivan Lopez-Espejo, e-mail: ivl@es.aau.dk

Dr. Sven Ewan Shepstone (Bang & Olufsen)

ECTS: 2.0

Time: 28 April, 01, 03 May 2023

Place: Fredrik Bajers Vej 7A/4-106

Number of seats: 50

Deadline: 07 April 2023

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.

Teacher: Ivan Lopez Espejo
Teacher: Zheng-Hua Tan

Design and Analysis of Experiments (2023)

Welcome to Design and Analysis of Experiments (2022)

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.

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 lectures. Lecture 0 below is not mandatory and does not count for participation in the minimum required attendance of 9 lectures. I.e. you are not required to follow lecture 0, but if you are new to R it is a good idea to attend lecture 0.
Hand in a statistical analysis done in the last two lectures (needs to be passed)

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

ECTS:

Dates: 12, 13, 15, 20, 22, 27, 28 September, 04, 05, 11, 12, 18, 19 October 2023

Time: afternoon 12.15 - 16.00

Place:

12, 13, 15, 22, 27, 28 September: Fredrig Bajers Vej 7A, room 4-103

20 September, 4, 5, 11, 12, 18, 19 October: Fredrik Bajers Vej 7C, room 3-204

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Important information concerning PhD courses:

Machine Learning (2023)

Welcome to Machine Learning (2023)

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:

Lecturers:

zt@es.aau.dk

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Place:

Aalborg University, Fredrik Bajers Vej 7A/4-106

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Important information concerning PhD courses:

Teacher: Ivan Lopez Espejo
Teacher: Zheng-Hua Tan

Mixed Models with Biomedical and Engineering Applications (2023)

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.

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Important information concerning PhD courses:

Teacher: Rasmus Waagepetersen

Nonlinear Differential Equations and Dynamical Systems (2023)

Welcome to Nonlinear Differential Equations and Dynamical Systems (2023)

Description:

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Important information concerning PhD courses:

Teacher: Lisbeth Fajstrup
Teacher: Rafal Wisniewski

Scientific Computing using Python – 1. Python + Scientific Computing (2023)

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, Katrine Søndergaard kes@adm.aau.dk.

Welcome to Scientific Computing using Python – 1. Python + Scientific Computing (2023)

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:

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

This specific course content is as follows:

The Python language

Course introduction

Historical overview of scientific computing and high performance computing

Python development environment

Python from above
Data types, built-in functions
Branching and looping
Functions (definition, built-in, lambda)
Modules and packages

Debugging and testing

Pytest
Doctest
Pdb (breakpoints and post-mortem debugging)

Basic scientific computing packages

NumPy (numerical computing - array-based - vectorization)
SciPy (various tools for integration, optimization, etc.)
Matplotlib (data visualization) + other visualisation packages
H5py (data storage/access via HDF)
Documentation using Sphinx

Scientific computing

Basic issues related to computational sciences such as

Floating-point representation
Numerical accuracy and condition number
Cancellation
Algorithmic complexity

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 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:

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 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:

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

Organizer:

Thomas Arildsen - tari@its.aau.dk

Lecturers:

Thomas Arildsen

ECTS:

2,5

Time:

June 12, 13 and 14

Place:

Aalborg University, Kroghstræde 1/1.007 - Mozart

Zip code:

9220

City:

Aalborg

Number of seats:

25

Deadline:

May 22, 2023

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.

Teacher: Thomas Arildsen

Scientific Computing using Python – High Performance Computing in Python (2023)

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, Katrine Søndergaard kes@adm.aau.dk.

Welcome to Scientific Computing using Python – High Performance Computing in Python (2023)

Description:

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 - briefly: 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 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:

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

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NB This course is only for PhD Students at Aalborg University

Important information concerning PhD courses:

Teacher: Thomas Arildsen
Teacher: Sokol Kosta

Signal and Spectral Analysis: extracting information from noisy data (2023)

For inquiries regarding registration or cancellation, please contact the PhD administration, Katrine Søndergaard kes@adm.aau.dk.

Welcome to Signal and Spectral Analysis: extracting information from noisy data (2023)

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. Professor Jesper Rindom Jensen - jrj@create.aau.dk

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Teacher: Jesper Rindom Jensen