Content of Learning Activity

This course offers a comprehensive introduction to statistical and machine learning methods. Topics covered include:
- Foundations: review of probability and statistics, nonlinear optimization (gradient descent, Newton’s method).
- Supervised learning (regression and classification): training and test errors, examples of parametric and non-parametric models (e.g., polynomial regression, k-nearest neighbors), model evaluation and selection, bias-variance trade-off, underfitting and overfitting.
- Resampling methods: cross-validation, bootstrap.
- Linear models: linear regression and interpretation, subset selection, regularization techniques (ridge and LASSO).
- Classification models: logistic regression, naive Bayes, linear and quadratic discriminant analysis.
- Nonlinear models: decision trees for regression and classification (based on entropy and Gini index), neural networks.
- Ensemble methods: bagging, random forests, boosting.
- Unsupervised learning: dimensionality reduction, principal component analysis.
- Additional topics, such as support vector machines or clustering methods, may be covered depending on available time.

Exercise sessions and the course project rely on standard scientific computing libraries in Python (numpy, pandas, scikit-learn, PyTorch).

Required Learning Resources/Tools

Slides, lecture notes, and exercise sheets are available on Moodle. The course material is in English.

Recommended Learning Resources/Tools

Not applicable

Other Recommended Reading

- An Introduction to Statistical Learning: with Applications in Python. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Springer.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd edition). Hastie, T., Tibshirani, R., & Friedman, J. (2009). Springer.
- CS229: Machine Learning – Lecture Notes. Ng, A., & Ma, T. (2023). Stanford University.
- Introduction to Probability for Data Science. Chan, S. H. (2021). Michigan Publishing Services.

Mode of delivery

• Face-to-face

Type of Teaching Activity/Activities

• Cours magistraux
• Travaux pratiques
• Projet sur ordinateur

Evaluations

The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)