Welcome to Advanced Spectral Estimation (2025)
Description: Spectral analysis is a fundamenal tool in a broad range of scientific disciplines, including telecommunications, chemistry, power electronics, and speech and audio processing. The course gives an overview of modern methods for spectral analysis for both stochastic and deterministic signals, including both parametric and non-parametric methods. More specifically, a number of methods based on different principles will be discussed, namely classical methods based on the Fourier transform, methods based on concepts from linear algebra such as shift-invariance and subspaces, optimal distortion-less filtering methods, and sparsity-based methods based on convex optimization and statistical principles. The properties of these methods will be analyzed and their application to real signal will be discussed.
Prerequisites: Basic probability theory, linear algebra, signal processing, and experience with MATLAB and/or Python programming.
Learning objectives:
1. Develop a deep understanding of the theoretical foundations of spectral analysis, including the distinction between stochastic and deterministic signals.
2. Gain proficiency in both classical methods based on the Fourier transform and modern techniques that leverage linear algebra, such as shift-invariance and subspace methods.
3. Differentiate between parametric and non-parametric methods for spectral analysis, and assess the advantages and limitations of each approach in various scientific applications.
4. Master the principles and applications of optimal distortion-less filtering methods in spectral analysis, and understand their significance in reducing noise and enhancing signal clarity.
5. Learn and apply sparsity-based methods, grounded in convex optimization and statistical principles, to effectively analyze sparse signals.
6. Critically evaluate the performance of different spectral analysis techniques, considering factors such as accuracy, computational efficiency, and robustness.
7. Develop the ability to apply various spectral analysis methods to real signals within the students problem domain.
8. Keep abreast of the latest advancements in spectral analysis methods and their applications, fostering the ability to adapt to emerging challenges and innovations in the field.
9. Prepare to conduct independent research in the field of spectral analysis, contributing to the development of new methods or the novel application of existing techniques to solve complex scientific problems.
Organizer: Jesper Rindom Jensen
Lecturers: TBA
ECTS: 3.0
Time: 25, 26, 27, 28, 29 August 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: 4 August 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: Spectral analysis is a fundamenal tool in a broad range of scientific disciplines, including telecommunications, chemistry, power electronics, and speech and audio processing. The course gives an overview of modern methods for spectral analysis for both stochastic and deterministic signals, including both parametric and non-parametric methods. More specifically, a number of methods based on different principles will be discussed, namely classical methods based on the Fourier transform, methods based on concepts from linear algebra such as shift-invariance and subspaces, optimal distortion-less filtering methods, and sparsity-based methods based on convex optimization and statistical principles. The properties of these methods will be analyzed and their application to real signal will be discussed.
Prerequisites: Basic probability theory, linear algebra, signal processing, and experience with MATLAB and/or Python programming.
Learning objectives:
1. Develop a deep understanding of the theoretical foundations of spectral analysis, including the distinction between stochastic and deterministic signals.
2. Gain proficiency in both classical methods based on the Fourier transform and modern techniques that leverage linear algebra, such as shift-invariance and subspace methods.
3. Differentiate between parametric and non-parametric methods for spectral analysis, and assess the advantages and limitations of each approach in various scientific applications.
4. Master the principles and applications of optimal distortion-less filtering methods in spectral analysis, and understand their significance in reducing noise and enhancing signal clarity.
5. Learn and apply sparsity-based methods, grounded in convex optimization and statistical principles, to effectively analyze sparse signals.
6. Critically evaluate the performance of different spectral analysis techniques, considering factors such as accuracy, computational efficiency, and robustness.
7. Develop the ability to apply various spectral analysis methods to real signals within the students problem domain.
8. Keep abreast of the latest advancements in spectral analysis methods and their applications, fostering the ability to adapt to emerging challenges and innovations in the field.
9. Prepare to conduct independent research in the field of spectral analysis, contributing to the development of new methods or the novel application of existing techniques to solve complex scientific problems.
Organizer: Jesper Rindom Jensen
Lecturers: TBA
ECTS: 3.0
Time: 25, 26, 27, 28, 29 August 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 15
Deadline: 4 August 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Jesper Rindom Jensen
Welcome to Introduction to Information Theory in Neuroscience (2025)
Description: In this course, we introduce information theoretic notions that are applicable to several neuroscience systems. Our focus will be on directed information measures, which are useful for establishing statistical relationships between time series data such as EEG. We will also introduce non-directed measures such as phase synchrony.
You will learn about concepts such as mutual information, transfer entropy, redundant and synergistic information, connectivity matrices and coupling strengths between time series. These concepts will be demonstrated on EEG data and you will be able to apply the tools on your own real-world physiological data.
Prerequisites: Basic courses on statistics and probability theory
Learning objectives: You will learn about concepts such as mutual information, transfer entropy, redundant and synergistic information, connectivity matrices and coupling strengths between time series. These concepts will be demonstrated on EEG data and you will be able to apply the tools on your own real-world physiological data.
Organizer: Jan Østergaard
Lecturers: Jan Østergaard
ECTS: 1.0
Time: 7, 8, 9 April 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 17 March 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: In this course, we introduce information theoretic notions that are applicable to several neuroscience systems. Our focus will be on directed information measures, which are useful for establishing statistical relationships between time series data such as EEG. We will also introduce non-directed measures such as phase synchrony.
You will learn about concepts such as mutual information, transfer entropy, redundant and synergistic information, connectivity matrices and coupling strengths between time series. These concepts will be demonstrated on EEG data and you will be able to apply the tools on your own real-world physiological data.
Prerequisites: Basic courses on statistics and probability theory
Learning objectives: You will learn about concepts such as mutual information, transfer entropy, redundant and synergistic information, connectivity matrices and coupling strengths between time series. These concepts will be demonstrated on EEG data and you will be able to apply the tools on your own real-world physiological data.
Organizer: Jan Østergaard
Lecturers: Jan Østergaard
ECTS: 1.0
Time: 7, 8, 9 April 2025
Place: Aalborg University
Zip code: 9220
City: Aalborg
Maximal number of participants: 30
Deadline: 17 March 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Jan Østergaard
Welcome to Reinforcement Learning (2025)
Description: An intelligent system is expected to generate policies autonomously to achieve a goal, which is mostly to maximize a given reward function or minimize a given cost function.
Reinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays, reinforcement learning techniques are successfully applied in various engineering fields, including robotics (DeepMind’s walking robot) and computers playing games (AlphaGo and TD-Gammon).
Developed independently from reinforcement learning, dynamic programming is a set of algorithms in optimal control theory that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their relations to each other and on their applications to similar problems.
The course consists of the following topics:
Markov decision processes, dynamic programming for infinite time and stopping time, reinforcement learning, and verification tools for reinforcement learning.
Prerequisites: Basic knowledge of mathematics: calculus and probability
Learning objectives:
- General Introduction to machine learning herein Reinforcement Learning
- Markov Decision Processes and Dynamic Programming
- Reinforcement Learning with Temporal-Difference Learning
- Policy prediction with approximation
- Verification tool UPPAAL for model-based reinforcement learning
Key literature: TBA
Organizer: Rafal Wisniewski
Lecturers: Kim Guldstrand Larsen, Zheng-Hua Tan, Rafal Wisniewski, Marius Mikucionis, Abhijit Mazumdar
ECTS: 2.0
Time: 31 March to 4th April 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 40
Deadline: 10 March 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
Description: An intelligent system is expected to generate policies autonomously to achieve a goal, which is mostly to maximize a given reward function or minimize a given cost function.
Reinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays, reinforcement learning techniques are successfully applied in various engineering fields, including robotics (DeepMind’s walking robot) and computers playing games (AlphaGo and TD-Gammon).
Developed independently from reinforcement learning, dynamic programming is a set of algorithms in optimal control theory that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their relations to each other and on their applications to similar problems.
The course consists of the following topics:
Markov decision processes, dynamic programming for infinite time and stopping time, reinforcement learning, and verification tools for reinforcement learning.
Prerequisites: Basic knowledge of mathematics: calculus and probability
Learning objectives:
- General Introduction to machine learning herein Reinforcement Learning
- Markov Decision Processes and Dynamic Programming
- Reinforcement Learning with Temporal-Difference Learning
- Policy prediction with approximation
- Verification tool UPPAAL for model-based reinforcement learning
Key literature: TBA
Organizer: Rafal Wisniewski
Lecturers: Kim Guldstrand Larsen, Zheng-Hua Tan, Rafal Wisniewski, Marius Mikucionis, Abhijit Mazumdar
ECTS: 2.0
Time: 31 March to 4th April 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 40
Deadline: 10 March 2025
Important information concerning PhD courses:
There is 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 the 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 of the course.
We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk When contacting us please state the course title and course period. Thank you.
- Teacher: Kim Guldstrand Larsen
- Teacher: Abhijit Mazumdar
- Teacher: Marius Mikucionis
- Teacher: Zheng-Hua Tan
- Teacher: Rafal Wisniewski