Statistical Machine Learning

5 credits

Syllabus, Master's level, 1RT700

A revised version of the syllabus is available.
Code
1RT700
Education cycle
Second cycle
Main field(s) of study and in-depth level
Computer Science A1N, Data Science A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Technology A1N
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 8 March 2016
Responsible department
Department of Information Technology

Entry requirements

120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming.

Learning outcomes

Students who pass the course should be able to:

  • Structure and divide statistical learning problems into tractable sub-problems, formulate a mathematical solution to the problems and implement this solution using statistical software.
  • Use and develop linear and nonlinear models for classification and regression.
  • Describe the limitations of linear models and understand how these limitations can be handled using nonlinear models.
  • Explain the basic ideas of Bayesian modelling and be able to use them for classification and regression.
  • Explain how the quality of a model can be evaluated by use of cross validation, and specifically the trade-off between bias and variance.
  • Explain the challenges with high dimensional data and have a basic understanding of dimensionality reduction.
  • Use principal component analysis and clustering to visualize data and find groupings in data.

Content

This is an introductory course in statistical machine learning, focusing on classification and regression. Linear regression (traditional and Bayesian), classification via logistic regression, linear discriminant analysis, Gaussian processes, kernel methods, cross validation, model selection, regularization (ridge regression and LASSO), regression and classification trees, principal component analysis and clustering. Applying the methods to real data.

Instruction

Lectures, problem solving sessions (both with and without computer) and homework assignments.

Assessment

Written exam (4 credits) and homework assignments (1 credit).

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