The basics of the probabilistic method.

Probabilistic modelling.

Dimensionality reduction.

Graphical models.

Hidden Markov models.

Expectation-Maximization.

Variational Inference.

Networks in variational inference.

After passing the course, the student should be able to

• explain and justify several important methods for machine learning
• give an account of several types of methods and algorithms that are used in the field of deterministic inference methods
• implement several types of methods and algorithms that are used in the field based on a high-level description
• extend and modify the methods that the course deals with

in order to be able to do a degree project in deterministic inference methods.

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Students at KTH with a permanent disability can get support during studies from Funka:

Funka - compensatory support for students with disabilities

A, B, C, D, E, FX, F

• LAB1 - Labs, 4.0 credits, Grading scale: A, B, C, D, E, FX, F
• TEN1 - Exam, 3.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.