IT Expertise - Machine Learning

General Data
Academic program	Incoming Exchange Student Courses			:
Type d'EC	Classes (LIIEXP08EMachineLearning)
Lectures : 20h00 Total duration : 26h00	Status : Obligatoire	Period : Semester 8_Robotique and Automation	Education language : English

Learning Outcomes
* 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.

Learning Outcomes

* 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.

Content
* 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...)

Content

* 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...)

Pre-requisites / co-requisites
* Matrix algebra and calculus * Probability and Statistics * Algorithmic and programming

Bibliography
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)