INFO4222 : Fondements de l'Apprentissage
Foundations of Machine learning
- Responsable(s) :
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- Daniel Hirschkoff
- Enseignant(s) :
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- Titouan Vayer
- Mathurin Massias
Niveau
M1+M2
Discipline
Informatique
ECTS
3.00
Période
2e semestre
Département
Localisation
Site Monod
Année
2024
Public externe (ouverts aux auditeurs de cours)
Informations générales sur le cours : INFO4222
Content objectif
The aim of this course is to introduce the basic theory and algorithms of Machine Learning. Topics to be taught :
- General introduction to Machine Learning: learning settings, curse of dimensionality, overfitting/underfitting, etc.
- Overview of Supervised Learning Theory: True risk versus empirical risk, loss functions, regularization, bias/variance trade-off, complexity measures, generalization bounds.
- Linear/Logistic/Polynomial Regression: batch/stochastic gradient descent, closed-form solution.
- Sparsity in Convex Optimization.
- Support Vector Machines: large margin, primal problem, dual problem, kernelization, etc.
- Neural Networks, Deep Learning.
- Theory of boosting: Ensemble methods, Adaboost, theoretical guarantees.
- Non-parametric Methods (K-Nearest-Neighbors)
- Domain Adaptation
- Optimal Transport
Content prerequis
Basic knowledge of probability theory, linear algebra and analysis over the reals
Content modalites
~20h of lectures + 20h of lab sessions
Content bibliographie
- Statistical Learning Theory, V. Vapnik, Wiley, 1998
- Machine Learning, Tom Mitchell, MacGraw Hill, 1997
- Pattern Recognition and Machine Learning, M. Bishop, 2013
- Convex Optimization, Stephen Boyd & Lieven Vandenberghe, Cambridge University Press, 2012.