Advanced Machine Learning (Autumn 2024)

Abstract

This course introduces Machine Learning methods and how they are used in active research. Methods will be described in a way to enable you to select the suitable tool for given application, and adapt accordingly.

Description

We build upon knowledge from the course "Linear Algebra and Probability", and assume that you have prior knowledge in programming. It is highly recommended to have taken the course "Introduction to Machine Learning".  
The course enables students to understand and implement advanced machine learning algorithms, and modify the learnt methods to analyze the outcome of various applications on different datasets. This includes the ability to select appropriate tools and reflect on the results.  
The first 10 weeks of the course consist of lectures and exercises twice a week, the remaining 4 weeks are reserved for the mandatory group project, which will be part of the oral exam.

Formal prerequisites

Linear Algebra and Probability

Intended learning outcomes

After the course, the student should be able to:

• Define and describe basic machine learning terms and methods.
• Develop and implement machine learning methods on your own in an appropriate programming language.
• Characterize, relate, and analyze central machine learning concepts and algorithms.
• Combine and modify machine learning methods to analyze practical datasets and reflect on the results.

Ordinary exam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.