Due to the COVID19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
Teacher(s)
Language
English
Main themes
 Learning as search, inductive bias
 Combinations of decisions
 Loss function minimization, gradient descent
 Performance assessment
 Instancebased learning
 Probabilistic learning
 Unsupervised classification
Aims
At the end of this learning unit, the student is able to :  
1 
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Students will have developed skills and operational methodology. In particular, they have developed their ability to:

Content
 Decision Tree Learning: ID3, C4.5, CART, Random Forests
 Linear Discriminants: Perceptrons, GradientDescent and LeastSquare Procedures
 Maximal Margin Hyperplanes and Support Vector Machines
 Deep Learning
 Probability and Statistics in Machine Learning
 Performance Assessment: Hypothesis testing, Comparing Learning Algorithms, ROC analysis
 Gaussian Classifiers, Fisher Linear Discriminants
 Bayesian Learning: ML, MAP, Optimal Classifier, Naive Bayes
 Instancebased learning: kNN, LVQ
Teaching methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
 Lectures
 Several projects including some theoretical questions and mostly practical applications.
Practical projects are submitted on line and evaluated on the Inginious platform.
Evaluation methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
The projects are worth 40 % of the final grade, 60 % for the final exam (closedbook).The miniprojects cannot be implemented again in second session.
The projects grades are fixed at the end of the semester and included as such in the global score for the second session.
The final exam is, by default, a written exam (on paper or, when appropriate, on a computer)
These evaluation rules are subject to possible updates due to the sanitary situation. In particular, the relative weights between the projects and the final exam could be adapted. Such possible updates would be notified to the students by a general announcement posted on the Moodle site of this course.
Online resources
Bibliography
Des ouvrages complémentaires sont recommandés sur le site Moodle du cours.
Additional textbooks are recommended on the Moodle site for this course.
Additional textbooks are recommended on the Moodle site for this course.
Teaching materials
 Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles depuis le site Moodle du cours.
 Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.
Faculty or entity
Force majeure
Teaching methods
Lectures are given online and can be followed remotely. Computing projects are submitted online on the Inginious platform.
Evaluation methods
The gobal grade for the course is only based on the projects (no final exam). The relative weights of the projects in the global grade is adapted as follows:
 project 1 = 15%
 project 2 = 15%
 project 3 = 10%
 project 4 = 15%
 project 5 = 45%
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Electrical Engineering
Master [120] in Mathematical Engineering
Master [120] in Data Science Engineering
Master [120] in Data Science: Information Technology
Master [120] in Statistic: General
Master [120] in Statistic: Biostatistics
Master [120] in Biomedical Engineering
Master [60] in Computer Science