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.
Lectures, presentations, work with computer-aided data analysis.
Literature and preparations
Specific prerequisites *
Courses in probability and statistics, liner algebra, calculus in one and several variables, numerical methods.
Calculus in one and several variables, linear algebra, numerical methods, differential equations, probability and statistics.
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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.
Examination and completion
Grading scale *
A, B, C, D, E, FX, F
Grading scale: A, B, C, D, E, FX, F
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
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Opportunity to raise an approved grade via renewed examination