Machine Learning for Engineers

EE-613 / 4 credits

Teacher(s): Calinon Sylvain, Canévet Olivier, Odobez Jean-Marc, Villamizar Michael

Language: English

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