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 *

This course presents an overview of the most important methods of the modern theory of statistical learning. 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. To pass the course, the student should be able to do the following:

• explain the difference between unsupervised and supervised learning

• know the underlying mathematical relationships within and a cross statistical learning algorithms and the paradigms of supervised and unsupervised learning along with their strengths and weaknesses

• identify the correct statistical tool for a data analysis problem in the real world based on reasoned argument

• use  algorithmic models treating the data mechanism as unknown

• develop accurate and informative alternatives to data modelling on big and complex as well as on smaller data sets

• design and implement various statistical learning algorithms in a range of real-world applications

• design test procedures in order to evaluate a model, optimise the models learned and report on the expected accuracy that can be achieved by applying the models

• read current research papers and understand the issues raised by current research.

  To receive the highest grade, the student should in addition be able to do the following:

• combine several models in order to gain better results.

Course Disposition

Lectures, presentations, work with computer-aided data analysis.

Specific prerequisites *

Courses in probability and statistics, liner algebra, calculus in one and several variables, numerical methods.

Recommended prerequisites

Calculus in one and several variables,  linear  algebra,   numerical methods,  differential equations,  probability and statistics.

Equipment

No information inserted

Literature

An introduction to Statistical Learning, by G. James, D. Witten, T. Hastie, R. Tibshirani,  Springer Verlag, and additional reading available on the course web   page.

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.

Other requirements for final grade *

Written exam, assignments.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Pierre Nyquist

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.

Offered by

SCI/Mathematics

Main field of study *

Mathematics

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Timo Koski (tjtkoski@kth.se)

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.