A second course in machine learning, giving a broadened and deepened introduction to the area.
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2021
Content and learning outcomes
The basics of the probabilistic method.
Hidden Markov models.
Networks in variational inference.
Intended learning outcomes
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.
Preparations before course start
LiteratureNo information inserted
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Examination and completion
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.
No information inserted
Language Of Instruction
mladv21 (Start date 01/11/2021, English)