Recommended prerequisite for participation in the module

The course builds on knowledge obtained during the bachelor courses in “Linear Algebra” and “Introduction to Probability and Applied Statistics“.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Must have knowledge of data modelling in form of preparing data, modelling data, and evaluating and disseminating the results.
• Must have knowledge about key machine learning concepts such as feature extraction, cross-validation, generalization and over-fitting, prediction and curse of dimensionality.
• Must have knowledge about different machine learning principles, algorithms, techniques and be able to define and describe fundamental problems and consequences within machine learning.
• Must have knowledge about basic recommender system principles, techniques, algorithms and be able to define and describe fundamental problems and consequences within these.

Skills

• Must be able to discuss how the data modelling methods work and describe their assumptions and limitations.
• Must be able to map practical problems to standard data models such as regression, classification, density estimation, clustering and association mining.
• Must be able to select and apply a range of different machine learning algorithms and techniques on specific problems.
• Must be able to select and apply the basic recommender system algorithms and techniques on specific problems.

Competences

• Must have the competency to solve machine learning related problems in a practical context.
• Must have the competency to apply machine learning algorithms and analyse the results

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Exam

Exams

Name of exam	Machine Learning
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures