# Coursebooks 2017-2018

## Statistical learning

#### Lecturer(s) :

Thibaud Emeric Rolland Georges

English

#### Summary

An introduction to statistical methods for supervised and unsupervised learning.

#### Content

• Introduction: supervised and unsupervised learning, motivating exemples, train and test errors, bias-variance tradeoff, model complexity and overfitting, k-nearest neighbors;
• Regression: linear regression, model selection, Ridge and Lasso methods, non-linear models;
• Classification: linear discriminant analysis, logistic regression;
• Resampling methods: cross-validation, bootstrap;
• Tree-based methods: classification and regression trees, bagging, random forests;
• Boosting;
• Support vector machines: definition, kernel trick;
• Unsupervised learning: principal component analysis, k-means, Gaussian mixtures and the EM algorithm;
• Other topics as time permits.

#### Learning Prerequisites

##### Recommended courses

Analysis, Linear Algebra, Probability, Statistics, Linear Models

#### Learning Outcomes

By the end of the course, the student must be able to:
• Formulate appropriate models for empirical data
• Justify the choice of a model/technique to analyze empirical data
• Analyze empirical data using supervised and unsupervised learning methods
• Implement statistical learning algorithms
• Estimate the parameters of a statistical model
• Interpret the fit of a model to data

#### Teaching methods

Ex cathedra lectures, exercises and computer practicals in the classroom and at home.

#### Assessment methods

Continuous control, final exam.

Second session: from the rulebook of the Section of Mathematics (art. 3 al. 5), the teacher decides of the form of the exam and communicates it to the concerned students.

#### Supervision

 Assistants Yes

#### Resources

No

##### Bibliography

• James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning, with Applications in R. Springer.
• Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second edition. Springer.
• Bishop, C. M. (2006) Pattern Recognition and Machine Learning. Springer.
• Shalev-Shwartz, S. and Ben-David, S. (2014) Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

### Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Under construction

Lecture
Exercise, TP
Project, other

### legend

• Autumn semester
• Winter sessions
• Spring semester
• Summer sessions
• Lecture in French
• Lecture in English
• Lecture in German