SF2957 Statistical Machine Learning 7.5 credits
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Content and learning outcomes
This course presents an overview of advanced methods of statistical machine learning. Topics covered include classical and Bayesian decision theory, deep learning for regression and classification, Gaussian processes for regression and classification, clustering, reproducing kernel Hilbert spaces, reinforcement learning, and computational methods in machine learning. Computer-aided projects with a variety of datasets forms an essential learning activity.
Intended learning outcomes
After completion of the course, the student shall be able to:
- formulate and apply statistical decision theory
- formulate and apply advanced methods in statistical machine learning
- design and implement advanced methods in statistical machine learning for applications
Literature and preparations
- English B / English 6
- Completed basic course in numerical analysis (SF1544, SF1545 or equivalent)
- Completed basic course in probability theory and statistics (SF1922, SF1914 or equivalent)
- Completed advanced course in probability theory (SF2940 or equivalent)
Completed courses SF2935 Modern methods in statistical learning or equivalent, and SF2955 Computer intensive methods in statistics or equivalent.
Various books and lecture notes presented on the course web page.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- PRO1 - Project, 3.0 credits, grading scale: P, F
- TENA - Written exam, 4.5 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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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.
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web SF2957