- français
- English
Coursebooks
Machine Learning for Engineers
EE-613
Lecturer(s) :
Calinon SylvainFleuret François
Odobez Jean-Marc
Language:
English
Frequency
Every 2 yearsRemarque
Every two years. Next time: Fall 2019Summary
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.Content
Fundamentals
- Notion of learning, cross validation and performance evaluation
- Recalls in probability and information theory
- Optimization (gradient, newton, stochastic gradient, etc.)
Generative models
- Directed / non-directed models, conditional independence, naive Bayesian
- k-Mean, GMM, E-M
- PCA and probabilistic PCA
- Bayesian networks, belief propagation
- HMM and extensions
- Sub-space clustering
Regression
- Least-square + weighted least-square
- 2) GMR + GPR
Discriminative models
- SVMs and Kernelization (perceptron, PCA, etc.)
- Perceptron, MLP, convolution networks
- Decision trees
Meta-algorithms
- Bagging and boosting
- Feature selection, regularization and sparsity
Keywords
Machine learning, pattern recognition, regression.
Learning Prerequisites
Required courses
At least one prior course in probabilities, linear algebra and programming (C, Java or equivalent).
Learning Outcomes
By the end of the course, the student must be able to:- Select appropriately in practice standard learning-based inference techniques for regression, classification and density modeling.
Assessment methods
Multiple.
In the programs
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Machine Learning for Engineers - Lecture
28 Hour(s) - Practical work
28 Hour(s)
- Semester
Reference week
Lecture
Exercise, TP
Project, other
legend
- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German