MATH-412 / 5 credits

Teacher: Obozinski Guillaume Romain

Language: English

## Summary

A course on statistical methods for supervised and unsupervised learning.

## Required courses

Analysis, Linear Algebra, Probability and Statistics, Linear Models

## Important concepts to start the course

This is a statistics/mathematics course. Prior to following this course, the student must have very good knowledge of basic probabilty and statistics (statistical modeling and inference, linear regression).

## Learning Outcomes

By the end of the course, the student must be able to:

• Formulate appropriate models for empirical data
• Estimate the parameters of a statistical model
• Interpret the fit of a model to data
• Justify the choice of a model/technique to analyze empirical data
• Implement statistical learning algorithms
• Explain the mathematical/statistical mechanisms of most common machine learning algorithms

## Teaching methods

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

## Assessment methods

Written final exam (70%) + Project of implementation or application on real data of a model/algorithm based on a classical research paper describing an important method from the literature. (30%)

Seconde tentative : Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

## Supervision

 Office hours No Assistants Yes Forum Yes

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.
• Efron, B. and Hastie, T. (2016) Computer Age Statistical Inference: Algorithms, Evidence and Data Science. Cambridge University Press.
• Bishop, C. M. (2006) Pattern Recognition and Machine Learning. Springer.
• Kuhn, M. and Johnson, K. (2013) Applied Predictive Modeling. Springer.
• Shalev-Shwartz, S. and Ben-David, S. (2014) Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

## Notes/Handbook

A polycopié will be available on Moodle.

## In the programs

• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Statistical machine learning
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 MAA331 9-10 10-11 MAA112 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22

Wednesday, 8h - 10h: Lecture MAA331

Thursday, 10h - 12h: Exercise, TP MAA112