Machine Learning (Autumn 2022)

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 and 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 course gives an overview of fundamental concepts and reasoning behind machine learning methods, based especially on a probabilistic and decision-theoretic framework. Further, we discuss a broad range of classical machine learning methods such as 

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

Formal prerequisites

The course is mandatory for third semester in the BSc in Data Science and assumes students to have followed the courses Applied Statistics and Linear Algebra and Optimisation, or something equivalent.

Some prior experience with Python will also be assumed.

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

The course will comprise around 10 weeks of lectures and exercise sessions and around 4 weeks of project work.

Course literature

Aurélien Géron (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly.

Christopher Bishop (2006). Pattern Recognition and Machine Learning. Springer. 

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.

reexam

Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.

Time and date

Ordinary Exam - submission Fri, 6 Jan 2023, 08:00 - 14:00
Ordinary Exam Mon, 23 Jan 2023, 09:00 - 21:00
Ordinary Exam Tue, 24 Jan 2023, 09:00 - 21:00
Ordinary Exam Wed, 25 Jan 2023, 09:00 - 21:00
Ordinary Exam Thu, 26 Jan 2023, 09:00 - 21:00
Ordinary Exam Fri, 27 Jan 2023, 09:00 - 21:00
Reexam Mon, 13 Mar 2023, 08:00 - 21:00
Reexam Tue, 14 Mar 2023, 08:00 - 21:00