TNA011 · 6 credits · Basic level (G1F) · Matematik, Tillämpad matematik
Applied Mathematics with AI
Active · next start VT 2027
40%
passed the latest regular sitting
DAK1 · 17 Aug 2026 · 5 students
47%
passed the regular sittings, all sittings over the last 5 years
DAK1 · 64 students
92%
Harder than 92% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 64 students.
Part of 2 programmes
Other module
DAK1 Test in computer lab, introduction to Matlab
- G
- U
Main rounds
47% passed · 64 students
- 40%5
- 47%59
Later rounds
79% passed · 19 students
- 79%19
- Pass a later round: 79% (19 students), compared with 47% (64 students) on the ordinary exam
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Mini group project; Oral and written presentation and opposition
- G
- U
Main rounds
95% passed · 65 students
- 95%65
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%3
Other module
DAK2 Test in computer lab, mathematics in AI
- G
- U
Main rounds
55% passed · 49 students
- 57%7
- 55%42
Later rounds
56% passed · 18 students
- 56%18
- Pass a later round: 56% (18 students), compared with 55% (49 students) on the ordinary exam
About the course
Aim
The course is intended to bridge between basic mathematic courses and courses within science and technology. Students will aim to improve their ability in using mathematical results and computational software, as MATLAB and Python, to study, analyse, and to some degree, solve mathematical problems from AI applications. The course introduces basic mathematical concepts within AI.
After the course, the student should be able to:
- model mathematically certain problems in information technology and AI;
- use simpler AI models and reflect over their capabilities and limitations;
- use MATLAB and Python as a natural tool to solve computational problems, including: work with matrices and systems of linear equations, formulate and solve least squares problems;
- select a suitable method to study mathematical problems that arise in different contexts of applied mathematics and AI;
- structure and present mathematical ideas in both written and oral form, and conduct a discussion about the content;
- give constructive feedback on technical reports.
Content
Introduction to MATLAB, introduction to Python and simpler programming with a focus on computation. Introduction to mathematical concepts and modelling in AI. Numerical methods: linear algebra, mappings, systems of equations, least squares problems, computations with vectors and matrices. Treatment of application problems. Linear regression; clustering; classification; and neural networks. Report writing in Swedish and written feedback. Oral presentation.
- Examiner
- Berkant Savas
- Scheduled hours
- 37 h
- Main field of study
- Matematik, Tillämpad matematik