Course Syllabus for

Learning Theory and Reinforcement Learning
Inlärningsteori och förstärkningsinlärning

EDA065F, 6 credits

Valid from: Spring 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2020-08-26

General Information

Division: Computer Science (LTH)
Course type: Third-cycle course
Teaching language: English


The course aims to give an insight into the underlying mathematical and statistical principles for machine learning, as well as into the specific learning approach Reinforcement Learning.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must

Course Contents

The course is set up in three modules: In the first course module, we aim to ensure that all students master the basic mathematical tools (statistical framework, optimization, concentration) that constitute the foundations of the theory of Machine Learning. The second course module applies the tools introduced in the first module to recent solutions for supervised and unsupervised learning problems (SVM, Kernel methods, Deep learning, as well as clustering and cluster validation). The third course module contains an exhaustive introduction of theoretical and practical aspects of reinforcement learning (MDP, dynamic programming, Q-learning, policy-gradient, learning with function approximation, and recent Deep RL algorithms).

Course Literature

Lecture material (slides, articles, and literature recommendations) and hand-in assignments are distributed via the course homepage or the used and announced course content management system respectively.

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises, self-study literature review, miscellaneous. Each of the course modules is designed individually, containing some combination out of lectures, exercises, practical exercises, study material / reading advise, and homework assignments . A module consists of a two-day physical meeting with lectures and some form of exercises, as well as some homework assignments that the students can work on prior / after the meeting.

Examination Details

Examination formats: Written assignments, miscellaneous. The examination criteria aside the active participation in the physical meeting for each module are set by the teachers responsible for the respective module and communicated prior to or at the physical meeting for the module.
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Specific assumed prior knowledge is communicated through the course website per module, which is available well in advance. Generally the course requires at least some knowledge in probabilistic representation and reasoning, graphical models, and first order logic. Students are expected to have a background in Computer Science, Mathematics, Engineering Physics, Electrical Engineering or a closely related topic.

Course Occasion Information

Contact and Other Information

Course coordinator: Elin A. Topp <>
Web page:

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