TNM119 · 6 credits · Basic level (G2F) · Medieteknik
Introduction to Artificial Intelligence
Active · next start VT 2027
100%
passed the latest regular sitting
PRA1 · 26 Mar 2026 · 29 students
100%
passed the regular sittings, all sittings over the last 5 years
PRA1 · 66 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 1 programme
Hand-in or lab
PRA1 Project
- G
Main rounds
100% passed · 66 students
- 100%29
- 100%37
Later rounds
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Hand-in assignment
- G
Main rounds
100% passed · 65 students
- 100%29
- 100%36
Later rounds
No sittings with at least 5 students.
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
Hand-in or lab
LAB1 Laboration
- 5
- 4
- 3
Main rounds
100% passed · 63 students
- 100%31
- 100%32
Later rounds
No sittings with at least 5 students.
- Get 4 or 5: 49% (63 students)
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
About the course
Aim
The aim of the course is for the student to gain insights in artificial intelligence, including classical machine learning techniques, deep learning and generative models.
After finishing this course the student should be able to
- reason about different uninformed search techniques and apply them to common search problems
- reason about different machine learning techniques,
- reason on differences between classical machine learning techniques, deep learning, and generative models,
- identify the appropriate technique for solving AI-related optimization problems,
- apply common AI techniques to solve problems related to perception, reasoning and actions,
- reason about the challenges of keeping humans in the loop in autonomous systems,
- analyze and describe common ethical and societal challenges with artificial intelligence,
- reason about human responsibilities in AI design, such as lawfulness, fairness, interpretability and explainability.
Content
Fundamental concepts on history of AI and its significance, expert systems, uninformed search, supervised machine learning techniques, optimization techniques for AI applications such as regression and deep learning models, human-AI collaboration
- Examiner
- Elmira Zohrevandi
- Scheduled hours
- 52 h
- Main field of study
- Medieteknik