Statistical Machine Learning
Syllabus, Master's level, 1RT700
- 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
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- 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).