**Machine Learning**

Course offered in the second semester of the M1.

This course gives a general introduction to Machine Learning, from algorithms to theoretical aspects in

Statistical Learning Theory.

**Topics covered:**

• 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

• Metric Learning

• Optimal Transport

**Teaching methods:** Lectures and Lab sessions.

**Form(s) of Assessment:** written exam (50%) and project (50%)

**References:**

– 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.

– On-line Machine Learning courses: https://www.coursera.org/

**Expected prior knowledge:** basic mathematics and statistics – convex optimization