| Course label : | Refresher in mathematics |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S3_RMA - Refresher in mathematics |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
Reminders on linear algebra and applications: Discrete Fourier Transform, Singular Value Decomposition, linear regression, low rank approximations. Basics on optimization with constraints. Lagrange multipliers. Uzawa algorithm.
Educational goals
After successfully taking this course, a student should be familiar with fundamental concepts from linear algebra and nonlinear optimization which are relevant to data science.
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Exam1, (min) 0 - 20 (max)
Exam2, (min) 0 - 20 (max)
Passing grade 10/20
Online resources
Gene H Golub and Charles F Van Loan. Matrix computations. JHU press, 2013.
Pedagogy
24 hours, 12 hours lectures, 12 hours exercise session.
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Basic mathematical knowledge