Advanced Machine Learning, 6 credits

Main field of study

Course level

Advancement level

Course offered for

• Computer Science and Engineering, M Sc in Engineering
• Information Technology, M Sc in Engineering
• Computer Science and Software Engineering, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Probability theory and Statistics; Bayesian Learning; Machine Learning; Mathematical analysis; Linear Algebra; Basic programming.

Intended learning outcomes

The course presents the analysis of several large classes of models widely used in advanced machine learning, such as state-space models, gaussian processes, hidden Markov models, Bayesian networks, and Markov random fields. Students will learn about the structure and learning of these models, when they are applicable, how to use them in practical machine learning applications, and how to correctly interpret the results. The models are mainly analyzed from a Bayesian perspective.
After completing the course, the student should be able to:

• use the introduced model classes to accurately formulate and solve practical problems.
• learn the parameters and perform predictions in the presented models.
• evaluate and choose among the models within each class.
• implement the models and learning methods in a programming language. 

Course content

Bayesian learning summary, Gaussian processes, State-space models, Kalman filtering and smoothing, Particle methods, Graphical models, Bayesian networks, Markov models, Hidden Markov models, Markov random fields.

Teaching and working methods

The course consists of lectures, seminars and computer laboratory work. The lectures introduce concepts and theories that students then use in problem solving at the computer labs. Seminars comprise student presentations and discussion of computer lab reports.

Examination

Grades

Other information

Supplementary courses:
Text Mining, Visual Object Recognition and Detection

Department

Director of Studies or equivalent

Examiner

Education components

Course literature