ETE337 · 2 credits · Basic level (G1N) · Datavetenskap

Elements of AI, Part 2: Building AI

Active · next start HT 2026

100%

passed the latest regular sitting

UPG1 · 11 Oct 2026 · 21 students

100%

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

UPG1 · 1,099 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 · 1,099 students

11 Oct 2026100%21

4 Oct 2026100%19
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15 Feb 2024100%33
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15 Oct 2023100%39
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Later rounds

100% passed · 1,009 students

23 Aug 2026100%7
15 Aug 2026100%41
14 Jun 2026100%37
3 May 2026100%10
26 Apr 2026100%14
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12 Apr 2026100%18
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29 Mar 2026100%19
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19 Jan 2026100%6
14 Dec 2025100%15
7 Dec 2025100%14
30 Nov 2025100%16
23 Nov 2025100%10
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9 Nov 2025100%11
2 Nov 2025100%24
4 Aug 2025100%7
15 Jun 2025100%46
26 May 2025100%14
16 Apr 2025100%29
25 Mar 2025100%5
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9 Mar 2025100%5
25 Feb 2025100%11
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31 Dec 2021100%7
30 Nov 2021100%15

Pass a later round: 100% (1,009 students), compared with 100% (1,099 students) on the ordinary exam

Show small sittings (82)

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

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About the course

Aim

The aim of the course is for the student to understand how practical AI systems can be constructed using machine learning, understand the possibilities and limitations of the technology, and get an overview of different methods and techniques used for AI and 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 how optimization is used to train machine learning models.
Describe the importance of handling uncertainty in machine learning and how probability theory can be used for this purpose.
Give an account of common methods and models used in machine learning.

Content

The course consists of five parts:

Getting started with AI
Dealing with uncertainty
Machine learning
Neural networks
Conclusions

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

Syllabus on Studieinfo ↗