Machine Learning (Autumn 2020)

Abstract

This course gives a fundamental introduction to machine learning (ML) with an emphasis on statistical aspects. In the course, we focus on both the theoretical foundation for ML as well as the application of ML methods.

Description

The course enables students to analyse machine learning algorithms, implement abstractly specified machine learning methods in an imperative programming language, modify machine learning methods to analyse practical datasets and convey the results.

The subjects covered include linear models for regression and classification, neural networks, kernel methods and graphical models.

• Linear models for regression and classification
• Neural networks
• Kernel methods
• Decision theory
• Mixture Models and EM
• Ensemble methods
• Decision trees

Formal prerequisites

• The prerequisite required for admission to the course is Linear Algebra and Probability or similar.
  
• Some prior experience with Python will be assumed.
  
• It is recommended to have a basic knowledge of statistics. 

Intended learning outcomes

After the course, the student should be able to:

• Discuss, clearly explain, and reflect upon central machine learning concepts and algorithms.
• Choose among and make use of the most important machine learning approaches in order to apply (match) them to practical problems.
• Implement abstractly specified machine learning methods in an imperative programming language.
• Combine and modify machine learning methods to analyse practical dataset and convey the results.

Learning activities

Description:
The first 10 weeks will consist of lectures and exercise sessions. The last 4 weeks will consist of project work.

Course literature

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. ISBN 978-0387-31073-2

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 15%
• Exercises: 20%
• Project work, supervision included: 25%
• Exam with preparation: 20%

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.

Time and date