Courses
This course aims at providing an introduction to reinforcement learning (RL) by covering the main topics ranging from classic approaches to modern techniques, with a particular focus on the groundbreaking integration of deep learning techniques in reinforcement learning algorithms.
The course is organized into 11 lectures with the following topics:
- Lecture 01: Learning agents;
- Lecture 02: Markov Decision Processes (MDPs);
- Lecture 03: Dynamic Programming;
- Lecture 04: Multi-Armed Bandits and Exploration vs Exploitation;
- Lecture 05: Model-Free Evaluation;
- Lecture 06: Model-Free Control;
- Lecture 07: Reinforcement Learning with value function approximation;
- Lecture 08: Introduction to deep Reinforcement Learning;
- Lecture 09: Policy Gradient;
- Lecture 10: Introduction to Actor-Critic Approaches;
- Lecture 11: Deep Actor-Critic Algorithms.
At the end of the course, the students will have developed the required theoretical and empirical knowledge about classic and modern reinforcement learning (RL) approaches. They will have a good knowledge of the core problems and goals of RL, its current state-of-the-art, and its future challenges. From a practical point of view, they will be able to design, implement, and carry out RL experiments using common tools, e.g., python libraries for RL and optimization. They will be able to conduct their M.Sc. theses on topics related to RL, and they will have good starting knowledge of RL for a future career in research or industry.
The evaluation consists of a written exam and 2 coding assignments.
The exam is in English and lasts 2 hours. There will be an exam at the end of the Summer Semester and one at the beginning of the Winter Semester. It will consist of open questions and/or quick application of algorithms by hand;
The coding assignments will be based on the technical skills developed in the exercise session, and will consist of autonomous implementation of algorithms, execution of experiments, and presentation of obtained results in a report of 2 pages. The two coding assignments will give, respectively, an additional bonus of 0.3 and 0.4 to the final grade of the written exam.
The course is organized into 11 lectures and 12 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise session will consist of a quick refresh of the topic of the lecture and a guided coding session using the Python programming language to implement and run reinforcement learning experiments.
The main reference book for the course is:
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).
Additional references are:
- Szepesvári, Csaba. "Algorithms for reinforcement learning." Synthesis lectures on artificial intelligence and machine learning 4.1 (2010): 1-103. (https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf);
- Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.
Every Winter Semester we offer a course on Introduction to Artificial Intelligence (Intro2AI). The course aims to provide an introduction to AI by covering the main topics ranging from search and logic, to decision-making and learning. The course is highly recommended to whomever is interested in the exciting topic of AI for the current studies, and possibly for a future job in research or industry.
The course is organized into 12 lectures grouped into 4 categories:
Lecture 00: Introductory remarks;
Searching agents
Lecture 01: Intelligent agents;
Lecture 02: Classical search;
Lecture 03: Advanced search;
Logical agents
Lecture 04: Propositional logic;
Lecture 05: First-order logic;
Lecture 06: Planning;
Reasoning agents
Lecture 07: Uncertainty quantification and probabilistic reasoning;
Lecture 08: Decision theory;
Lecture 09: Reinforcement learning;
Lecture 10: Game theory;
Learning agents
Lecture 11: Machine learning;
Lecture 12: Deep learning;
At the end of the course, the students will have acquired the required theoretical and empirical knowledge about classic and modern Artificial Intelligence (AI) approaches. They will have a good knowledge of the core problems and goals of AI, its current state-of-the-art, and its future challenges. They will be able to conduct their M.Sc. theses on topics related to AI, and they will have good starting knowledge of AI for a future career in research or industry.
The evaluation consists of a written exam.
The exam is in English and lasts 2 hours. There will be an exam at the end of the Winter Semester and one at the beginning of the Summer Semester. It will consist of open questions and/or application of algorithms by hand.
The course is organized into 12 lectures and 12 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise sessions will aim at strengthening the knowledge of the topics presented in the lectures through a quick refresh and guided exercises.
The main reference book for the course is:
- Stuart, Russell, and Norvig Peter. "Artificial Intelligence A Modern Approach Third Edition." (2010). (https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf).
Additional references are:
- Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006. (https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf);
- Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009;
- Peterson, Martin. An introduction to decision theory. Cambridge University Press, 2017;
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).
