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EN
Machine LearningEA: Systèmes avancés 1 (MRI)
10ECTS (EU)
2Coefficient
21:20Hours
Séquencement
Learning objectives
The aim is to introduce the theoretical and technical concepts of data analysis through machine learning and to apply them to real data. To understand some basic mechanisms aimed at replicating so-called "smart" skills through a system. The implementation of machine learning techniques with Python language.
Prerequisites
| Module | Teaching unit | Semester |
|---|---|---|
| Algorithms and Programming | Mathematics and Computer Science 5 | S5 |
| Statistics and Random Processes | Mathematics and Computer Science 5 | S5 |
| Linear Optimization | Mathematics and Computer Science 6 | S6 |
Program
- Linear, nonlinear and regularization regression.
- Linear classification
- Bayesian Classification
- Neuron networks
- Analysis of the data structure.
- Deep learning.
Assessment
Final and mini-project review
References
- Introduction to Machine Learning, Azencott Chloé-Agathe, Dunod 2019.
- Data Science: basics and case studies: Machine Learning with Python and R, Lutz Michel, Biernat Eric, Eyrolles 2015.
Skills
3| Ref. | Verb | Description | Level |
|---|---|---|---|
| C1_1 | identify | concepts of databases | 1 |
| C3_1 | design | appropriate methods for a given problem | 2 |
| C3_2 | choose | methods for implementing an application | 2 |