INFO4222 : Fondements de l'Apprentissage

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