Course Syllabus for

Graphical Models, Bayesian Learning, and Statistical Relational Learning
Grafiska modeller, Bayesiansk inlärning och statistisk sambandsbaserad inlärning

EDA055F, 6 credits

Valid from: Autumn 2019
Decided by: Professor Thomas Johansson
Date of establishment: 2019-10-08

General Information

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


Graphical Models, Bayesian Learning and Statistical Relational Learning is a core course within the Wallenberg AI, Autonomous Systems and Software Program (WASP), more specifically within the the WASP-AI Graduate School track. The purpose of the course is to give a deepened understanding of the type of models and machine learning methods mentioned in the course title. These are different from, e.g., deep learning or reinforcement learning approaches to a degree, that motivates a respective specific course to broaden the participants' knowledge within the general core topic of machine learning within WASP-AI.


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 structured in three modules, dealing with graphical models, algorithms for doing inference in and learning of them, and the combination of logical and probabilistic approaches to reasoning. Topics to be discussed include probabilistic graphical models, causal models, interventional distributions and structural learning algorithms (module 1); Markov chain Monte Carlo, approximate message-passing, and variational inference, with a particular emphasis on inference in probabilistic graphical models (module 2); Generalized syntax and semantics of propositional and (especially) predicate logic, as well as major results about algorithmic decidability and efficiency for logical formalisms (module 3).

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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