ETE370 · 3 credits · Basic level (G1N) · Datavetenskap

Foundations of machine learning

Active · next start HT 2026

100%

passed the latest regular sitting

UPG1 · 15 Oct 2026 · 32 students

100%

passed the regular sittings, all sittings over the last 5 years

UPG1 · 553 students

This module only reports passing results, so the pass rate does not show how many did not pass.

Hand-in or lab

UPG1 Assignments

Main rounds

100% passed · 553 students

15 Oct 2026100%32

15 Sep 2026100%43
15 Jun 2026100%57
15 May 2026100%45
15 Apr 2026100%45
31 Jan 2026100%22
15 Jan 2026100%18
15 Oct 2025100%23
15 Sep 2025100%35
15 Jun 2025100%38
16 May 2025100%16
16 Apr 2025100%30
15 Jan 2025100%29
15 Oct 2024100%21
16 Sep 2024100%30
15 May 2024100%16
15 Jan 2024100%17
15 Oct 2023100%17
15 Jun 2023100%19

Later rounds

100% passed · 442 students

15 Aug 2026100%16
15 Mar 2026100%26
15 Feb 2026100%17
15 Dec 2025100%29
15 Nov 2025100%33
30 Apr 2025100%6
16 Mar 2025100%17
16 Feb 2025100%20
27 Jan 2025100%7
15 Dec 2024100%18
25 Nov 2024100%6
16 Nov 2024100%9
18 Jun 2024100%11
14 Jun 2024100%13
30 May 2024100%5
15 Apr 2024100%9
3 Apr 2024100%12
15 Mar 2024100%23
15 Feb 2024100%12
15 Dec 2023100%14
15 Nov 2023100%18
15 Sep 2023100%15
15 May 2023100%13
15 Apr 2023100%10
15 Feb 2023100%7
15 Jan 2023100%11
15 Dec 2022100%10
15 Nov 2022100%5
15 Oct 2022100%10
15 Sep 2022100%8
15 Jun 2022100%10
15 May 2022100%14
15 Mar 2022100%8

Pass a later round: 100% (442 students), compared with 100% (553 students) on the ordinary exam

Show small sittings (25)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

16 Mar 2026100%1
16 Feb 2026100%1
19 Jan 2026100%1
25 Jun 2025100%1
15 Apr 2025100%1
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26 Mar 2025100%2
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25 Dec 2024100%1
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About the course

Aim

The aim of the course is to explain what machine learning is, what types of machine learning that exist, their possibilities and limitations, and to get an overview of the different methods and techniques used for machine learning. After the course, the student will be able to:

describe what machine learning can be used for, as well as its possibilities and limitations.
describe how data is used to train a machine learning model to solve a given problem.
describe the conditions under which machine learning methods can be expected to perform well.
formulate machine learning as optimization problems.
distinguish between supervised and unsupervised machine learning.
evaluate the performance of a machine learning model.
account for common methods and models used in machine learning.

Content

The course consists of seven parts:

What is machine learning?
Supervised learning: a first approach
Parametric models and a statistical perspective on learning
Understanding, evaluating, and improving performance
Neural networks and deep learning
Generative models and learning from unlabeled data
User aspects of machine learning

Examiner: Fredrik Lindsten
Scheduled hours: 0 h
Main field of study: Datavetenskap

Syllabus on Studieinfo ↗