| Course label : | Machine learning 1 |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister LOUIS FILSTROFF / Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S1_ML1 - Machine learning 1 |
Education team
Teachers : Mister LOUIS FILSTROFF / Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
Introduction to machine learning (ML), emphasizing the link with the other classes within the programme of the master's degree (ML2, probability, optimization). Several hands-on practical sessions in Python are aimed at building knowledge on the main concepts, covering: - scikit-learn basics; - data cleaning; - basics in plotting; - designing, implementing, testing, and evaluating an ML pipeline.
Educational goals
After successfully taking this course, a student should be able to: - identify a category of problems, in light of the available data and the nature of the parameters to learn (classification, regression, dimensionality reduction); - understand the main objectives of machine learning (ML) and the main approaches; - know the basic principles behind classical ML algorithms; - understand the first limits and requirements to conduct a ML project; - understand the notion of error and the principle of (regularized) empirical risk minimization (ERM); - use Python to implement ML algorithms, test them, and evaluate their performances; - access and preprocess data using Python.
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Labs, grading scale: (min) 0 – 20 (max)
Exam, grading scale: (min) 0 – 20 (max)
Average passing grade = 10/20
1. Continuous assessment activities and duration:
- 1 intermediate written exam (20 min) (exam1, /10)
- 1 final written exam (1h) (exam2, /20)
- 2 graded lab reports (2h each) (lab1, lab2)
2. Computation of the overall grade:
- `Mark1 = 0.125*(exam1 * 2) + 0.375*exam2 + 0.25*lab1 + 0.25*lab2`
3. If Mark1 < 10/20:
- 2nde chance exam : 1 written exam (2h) (exam3)
- final grade after 2nd chance exam (if required): `Mark2 = 0.6 * Mark1 + 0.4 * exam3`
Online resources
- Shalev-Shwartz and Ben-David (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014 - Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: Springer.
Pedagogy
- Labs (6h) and tutorial sessions (2 x 2h). - Final exam (2h). - Language of instruction: English.
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 knowledge of a programming language such as Python; notions on algorithms and fundamental notions of mathematics (linear algebra, notions in probability and optimization).
Maximum number of registrants
Remarks
Continuous evaluation, based on: - lab report(s), 50% of the overall grade, grading scale: (min) 0 – 20 (max) - 2 exams, 50% of the overall grade, grading scale: (min) 0 – 20 (max) 2nd chance exam (session 2): - grade on 20 points - final grade for the course: 60% session 1 (grade at the continuous assessment), 40% session 2