UE Advanced Artificial Intelligence : Advanced Machine Learn

ECUE's code : KMUAAIU

This course belong to CHOIX Mineures DS4H - M1 LEA RFI which contains 44 ECUE

Structure

EUR DS4H

Disciplinary domain

Informatique , Mathématiques

Campus

Campus SophiaTech Les Lucioles , Campus Valrose

Course's level

Master 1 , Master 2

Semester

Semestre impair

Language

Anglais

PRESENTATION

This course will develop an introduction to ML, by reviewing the fundamental principles and methods. Broadly speaking, Machine learning (ML) is the scientific field aiming at building models and inferring knowledge by applying algorithms to data. Therefore, the process involves the (statistical) analysis of data, and the design of models, possibly predictive. During this course, we will be more interested in the framework of use of the different methods rather than their mathematical foundations or their effective computer implementations.

This minor is open to students from the DS4H, and SPECTRUM graduate schools. According to their cursus, each student have different need and their level could be quiet different. So each session will be divided in two modules :

One lecture for all students during approximately 1h to 1h30 ;
One tutorial during approximately 2 hours with 2 separate groups adapted to 2 differents levels : Python (advanced), Python (beginner).

Course's manager(s)

, Michel Riveill

In class

12h of directed studies

Distant

12h of lectures

PREREQUISITES

Before the start of the course, I must ...

Scientific Bachelor, Python programming, Prerequisites and main concepts of Minor AI Introduction

OBJECTIVES

By the end of this course, I should be able to...

o Know the principles of Deep Learning (neural network) o Know how to build models based on neural networks to process structured data, images or text

CONTENT

Session 1

- Course goals and outline ((Michel Riveill)

- An introduction to Natural Language Processing (Michel Riveill)
Session 2

Deep learning – General principles (Michel Riveill)
Session 3

Deep learning - Multi-Layers perceptron (Michel Riveill)
Session 4

Deep learning - Recommender Systems (Michel Riveill)
Session 5

Deep learning - Recurrent Neural Network (Michel Riveill)
Session 6

Deep learning - Convolutional Neural Network (Diane Lingrand)
Session 7

Deep learning – Model Explainability (Diane Lingrand)
Session 8

Deep learning - Reinforcement Learning (Diane Lingrand)

Access to complete Syllabus (Authentification required)

Important

This syllabus has no contractual value. Its content is subject to change throughout this year: be aware to the last updates