IT Expertise - Machine Learning

Données Générales
Programme Académique	Incoming Exchange Student Courses			Responsable(s) Module : BARILLON Cristelle
Type d'EC : Cours	IT Expertise - Machine Learning (LIIExp08EMachineLearning)
Cours : 20h00 Travail personnel : 6h00 Durée totale: 26h00	Status Obligatoire	Periode Semester 8_Robotic And Automation	Langue d'enseignement : English

Objectifs Généraux
* Understand and acquire knowledge of basic concepts in Machine Learning • Build hypothesis based on a context • Test models and evaluate the consistency of their results with the application objectives • Understand the stakes and risks of artificial decision making * Be able to analyse and solve ML related problems (text classification, image classification, recommendation, regression...), using for instance Scikit Learn, Keras and TensorFlow library or NLP specific tools.

Objectifs Généraux

* Understand and acquire knowledge of basic concepts in Machine Learning
• Build hypothesis based on a context
• Test models and evaluate the consistency of their results with the application objectives
• Understand the stakes and risks of artificial decision making

* Be able to analyse and solve ML related problems (text classification, image classification, recommendation, regression...), using for instance Scikit Learn, Keras and TensorFlow library or NLP specific tools.

Contenu
* Introduction to IA and Machine Learning ° Model Based Learning - main concepts and definitions ° Exact solution, iterative solutions, Gradient Descent ° First algorithms: Regressions - Linear Regression - Logistic Regression ° Data preprocessing ° Hyperparameter tuning * Towards Deep Learning ° more on preprocessing (categories encoding) and data sets ° From biological neuron to perceptron ° Multilayer Perceptron ° Convolutional Neural Networks ° Transfer Learning * Introduction to Natural Language Processing ° Some important ideas about NLP ° Example of statistical NLP (Multinomial Naive Bayes) ° NLP with Deep Learning (LSTM) * Other algorithms * Group project (text classification, image classification, recommendation, regression...)

Contenu

* Introduction to IA and Machine Learning
° Model Based Learning - main concepts and definitions
° Exact solution, iterative solutions, Gradient Descent
° First algorithms: Regressions
- Linear Regression
- Logistic Regression
° Data preprocessing
° Hyperparameter tuning

* Towards Deep Learning
° more on preprocessing (categories encoding) and data sets
° From biological neuron to perceptron
° Multilayer Perceptron
° Convolutional Neural Networks
° Transfer Learning

* Introduction to Natural Language Processing
° Some important ideas about NLP
° Example of statistical NLP (Multinomial Naive Bayes)
° NLP with Deep Learning (LSTM)

* Other algorithms

* Group project (text classification, image classification, recommendation, regression...)

Prérequis
* Matrix algebra and calculus * Probability and Statistics * Algorithmic and programming

Bibliographie
the 100 page machine Learning Book, Andriy Burkov Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville (2016 - MIT press) Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, Aurélien Geron (2019 - O Reilly) Deep Learning With Python, François Chollet, (2021, Manning)