Entry requirements

120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus, Statistical Machine Learning, a course in several variable analysis and a course in introductory programming.

Learning outcomes

On completion of the course the student shall be able to:

• discuss and determine if an engineering-related problem can be formulated as a supervised or unsupervised machine learning problem, and to make this formulation.
• discuss similarities and differences (both practical and theoretical) between probabilistic and "traditional" machine learning methods.
• account and argue for a probabilistic approach, and be able to interpret and explain the outcome from probabilistic machine learning methods.
• analyze, implement and use the probabilistic models and methods that are included in the course.
• critically examine and provide constructive criticism on other student's reports about machine learning.

Content

This is an advanced course in machine learning, focusing on modern probabilistic/Bayesian methods: Bayesian linear regression and graphical models, as well as methods for exact and approximate inference in such models: Monte Carlo methods and moment matching. The course also contains necessary probability theory.

Instruction

Lectures, problem solving sessions (both with and without computer), laboratory work, feedback on written assignments.

Assessment

Written exam (3 credits) combined with oral and written presentation of assignments (2 credits). 

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course cannot be included in the same degree as 1RT003 Advanced probabilistic machine learning.