SF2935 Modern Methods of Statistical Learning 7.5 credits

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Headings with content from the Course syllabus SF2935 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

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.

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.

Course disposition

No information inserted

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

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

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 web

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 SF2935

Mathematics

Education cycle

Second cycle

No information inserted

Contact

Pierre Nyquist (pierren@kth.se)