TDDE56 · 6 credits · Basic level (G2F) · Datateknik, Datavetenskap
Foundations of AI and Machine Learning
Active · next start HT 2026
96%
passed the latest regular sitting
UPG1 · 1 Nov 2024 · 47 students
99%
passed the regular sittings, all sittings over the last 5 years
UPG1 · 141 students
13%
Harder than 13% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 47 students.
Part of 6 programmes
Hand-in or lab
UPG1 Hand-in assignments (online material)
- G
- U
Main rounds
99% passed · 141 students
- 96%47
- 100%40
- 100%23
- 100%31
Later rounds
79% passed · 28 students
- 100%16
- 50%12
- Pass a later round: 79% (28 students), compared with 99% (141 students) on the ordinary exam
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%4
- 0%2
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG2 Individual reflection paper after the project
- G
- U
Main rounds
71% passed · 146 students
- 93%43
- 67%43
- 58%60
Later rounds
84% passed · 61 students
- 95%19
- 89%9
- 100%8
- 68%25
- Pass a later round: 84% (61 students), compared with 71% (146 students) on the ordinary exam
Show small sittings (5)
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%3
- 100%1
- 100%1
- 75%4
About the course
Aim
On completion of the course, the student should be able to:
- explain central concepts, methods and models in AI and machine learning
- train machine learning models to solve simple problems and explain how data and optimisation are used in this context
- evaluate the performance of machine learning models with suitable methods and interpret the results of such evaluations
- model and solve simple problems using common AI methods
- apply AI and machine learning in the field of technology and critically assess such applications
- problematise ethical, societal and environmental aspects of applications of AI and machine learning
Content
- central concepts, methods and models in AI and machine learning
- training and evaluation of machine learning models
- software and data sets
- applications of AI and machine learning within the chosen field of technology
- Examiner
- Dominik Drexler
- Scheduled hours
- 30 h
- Main field of study
- Datateknik, Datavetenskap