Machine Learning
Information
| Teacher coordinator | Clément Chatelain |
| Teacher(s) | Clément Chatelain, Benoît Gaüzère, Simon Bernard |
| Language | English |
| Credits | 4.5 |
| Teaching | Lectures : 18h Exercises : 18h |
| Option | Data science |
| Web site | https://moodle.insa-rouen.fr/course/view.php?id=200 |
Aim and objective
The purpose of this lecture is to familiarize the student with learning and data mining methods on huge amount of data.
Outcome learning
- INSA reference data :
- Concevoir un système d'ingénierie des données [3P]
- Déterminer des classes de problèmes [3P]
- CNISF reference data :
- J40K [1P]
- J10C [2I]
Course description
- Kernel machine (SVM)
- Deep learning (CNN, LSTM, etc.)
- Decision trees and random forests
Prerequisites
- Introduction to machine learning
Bibliography
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, springer, 2001
- John Shawe-Taylor and Nello Cristianini Kernel Methods for Pattern Analysis, Cambridge University Press, 2004
- Bernhard Schölkopf and Alex Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
Assessment
- Project: 40%
- Final practical exam: 60%
