Number of hours
- Lectures 0
- Projects 0
- Tutorials 16.0
- Internship 0
- Laboratory works 12.0
- Written tests 0
ECTS
ECTS 2.0
Goal(s)
Learn some basic algorithms for Machine Learning and deep learning
Learn to design the right Machine Learning method according to a given dataset
Learn to evaluate the performances on a Machine Learning system
Content(s)
0. Introduction to deep learning
1. K-NN and Bayesian classification
2. Linear regression
3. Logistic Regression
4. SVM Classifier / Principal Component Analysis / Kmeans clustering
5. Neural Networks
6. Convolutional Neural Networks
7. Generative Neural Networks
8. Recurrent Neural Networks
Prerequisites
Python language MANDATORY
1st year mathematics course
Test
Semester 9 - The exam is given in english only 
Continuous assessment
Reports + MCQ
Contrôle continue : CC
Examen écrit Session1 : DS1
Examen écrit Session 2 : DS2
N1 = Note finale session 1
N2 = Note finale session 2
En présentiel :
N1 = 60% max(MCQs) + 40% max(labsession reports)
N2 = 60% max(MCQs) + 40% max(labsession reports)
En distanciel :
N1 = 100% Writen examen
N2 = 100% Writen examen
Commentaire :
Additional Information
This course brings 3.0 ECTS to students in UE Engineering ( SàC SCOG )
Semester 9 - This course is given in english onlyCourse list
Curriculum->Engineering degree->Semester 9
Curriculum->Double-Diploma Engineer/Master->Semester 9