This course presents an overview of the most important methods of the modern theory of statistical learning. Topics covered include supervised learning with a focus on classification methods, support vector machines, artificial neural networks, decision trees, boosting, bagging and methods of unsupervised learning with focus on K-means clustering and nearest neighbours. This course focuses primarily on the practical aspects of statistical learning. Computer-aided project work with a variety of datasets forms the essential learning activity.
SF2935 Modern Methods of Statistical Learning 7.5 credits
Information per course offering
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Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus SF2935 (Spring 2022–)Headings with content from the Course syllabus SF2935 (Spring 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
For the methods presented in the course, the student shall possess both theoretical and practical understanding of how the methods work, which ones to choose for a given problem and how to implement rudimentary versions of them. Computer-aided projects form an essential learning activity.
To pass the course the student shall be able to
- formulate and apply methods for supervised learning,
- formulate and apply methods for unsupervised learning,
- apply mathematical theory to analysis and explain properties of methods in statistical learning,
- design and implement methods in statistical learning for different tasks.
Literature and preparations
Specific prerequisites
- English B / English 6
- Completed basic course in probability theory and mathematical statistics (SF1918, SF1922 or equivalent).
Recommended prerequisites
Numerical methods (SF1544, SF1545 or similar), differential equations (SF1633, SF1683 or similar), probability and statistics (SF2940 or similar), regression analysis (SF2930 or similar).
Equipment
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Literature
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Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- TENA - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
- ÖVN1 - Assignments, 3.0 credits, grading scale: P, 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.
The written exam deals with concepts.
Opportunity to complete the requirements via supplementary examination
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Opportunity to raise an approved grade via renewed examination
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Examiner
Ethical approach
- 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
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Mathematics
Education cycle
Second cycle
Add-on studies
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