General Engineering Program
| General Data | ||||
|---|---|---|---|---|
| Academic program | General Engineering Program | Module Manager(s) :
GAGNEUX Antoine,JOUVE Christophe |
||
| EC Type : Lectures | Introduction To Data Science (LIIAem08EMachineLearning) | |||
|
Lectures : 8h00 Project : 10h00 Individual work : 8h00 Total duration: 18 |
Status
|
Period
Semester 8 |
Teaching language :
French |
|
| General objectives |
|---|
| The theme of the DATA course in digital coloring is machine learning. The goal is to understand the issues and initial concepts of machine learning. Skills: - Understand conceptual definitions - Understand mathematical definitions - Implement these definitions on simple examples - Implement on a complex example - Analyze the results and suggest improvements - Team working |
| Content |
|---|
| The course plan is as follows: - Linear regression and Gradient Descent - Logistic regression - Data: learning base vs test base - Over and under learning - Meta parameters - Perceptron - Neural networks The course will be enhanced with many exercises. The second part of the course is carried out in the form of a project whose objective is to implement the concepts seen in the first part. It is about carrying out a machine learning process on a real basis and studying the avenues for improvement. |
| Corequis |
|---|
| - Basics of algorithms. - Python basics |
| Bibliographie |
|---|
| Machine Learning course - Andrew Ng - Lecturesera - Stanford - 2021. Online : https://fr.coursera.org/learn/machine-learning Vidéos 3Blue1Brown series - Saison 3 - 2021 : Online ; https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi Deep Learning With Python, François Chollet, Edition Manning, 2022 |
| Assessment(s) | |||
|---|---|---|---|
| N° | Nature | Coefficient | Observable objectives |
| 1 | 1 | Project | |
| 2 | 1 | Continuous Assessment | |