Machine Learning for Engineers
EE-613 / 4 crédits
Enseignant(s): Calinon Sylvain, Canévet Olivier, Odobez Jean-Marc, Villamizar Michael
Langue: Anglais
Remark: Next time: Fall 2025
Frequency
Every 2 years
Summary
The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.
Content
Fundamentals
- Recalls in probability and information theory
- Notion of learning, cross validation and performance evaluation
- Optimization (gradient, Newton method, stochastic gradient, etc.)
Regression
- Least squares (Tikhonov regularization)
- Weighted least squares
- Iteratively reweighted least squares (IRLS)
- Tensor regression
- Gaussian mixture regression (GMR)
- Gaussian process regression (GPR)
Generative models
- Bayesian networks,
- Directed / non-directed models, conditional independence, Naive Bayes
- k-Means, Gaussian mixture model (GMM), Expectation-Maximization (EM)
- PCA and probabilistic PCA
- Hidden Markov model (HMM)
Discriminative models
- Logistic regression
- Decision trees, random forest
- Support vector machine (SVM) and Kernelization (PCA, etc.)
Deep learning
- Perceptron, Multi-Layer-Perceptron (MLP)
- Convolutional neural network (CNNs)
Meta-algorithms
- Ensemble methods
- Bagging
- Boosting
Learning Prerequisites
Required courses
- Undergraduate knowledge of probabilities, linear algebra, and statistics
- Python programming
Learning Outcomes
By the end of the course, the student must be able to:
- Understand the core principles of machine learning and of the different concepts and algorithms behind the different learning methodologies.
- Select appropriately in practice standard learning-based inference techniques for regression, classification and density modeling, including understanding the impact of different parameters
Assessment methods
Multiple.
Resources
Bibliography
Pattern Recognition and Machine Learning,
C. Bishop,
Springer, 2008
Ressources en bibliothèque
Moodle Link
Dans les plans d'études
- Nombre de places: 50
- Forme de l'examen: Multiple (session libre)
- Matière examinée: Machine Learning for Engineers
- Cours: 28 Heure(s)
- TP: 28 Heure(s)
- Type: optionnel
- Nombre de places: 50
- Forme de l'examen: Multiple (session libre)
- Matière examinée: Machine Learning for Engineers
- Cours: 28 Heure(s)
- TP: 28 Heure(s)
- Type: optionnel
- Nombre de places: 50
- Forme de l'examen: Multiple (session libre)
- Matière examinée: Machine Learning for Engineers
- Cours: 28 Heure(s)
- TP: 28 Heure(s)
- Type: optionnel