Hours:
20 hours (5 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
This course is an advanced course focusing on the intersection of Statistics and Machine Learning. The goal is to study modern statistical methods for supervised and unsupervised learning, and the underlying theory for those methods. Numerous illustrations in the context of signal / image processing will be provided. The students are expected to have basic knowledge of:

1. linear algebra
2. functional analysis.
3. basic probabilities concepts
4. foundations of machine learning concepts.

Course Contents in brief:

1. Reminders on multivariate statistics: ML, Bayesian theory, hypothesis testing, linear regression;
2. Robust theory: robust estimation, robust regression approaches;
3. Clustering: hierarchical clustering, DBSCAN, HDBSCAN algorithms;
4. Mixture models: GMM and more general distributions mixture, distribution fitting, parameters estimation, EM algorithms;
5. Model selection;
6. Applications to image and signal processing;

Schedule:

1. Day1 - 26 April 2021 - 9:30-12:00, 13:30-16:00
2. Day2 - 27 April 2021 - 9:30-12:00, 13:30-16:00
3. Day3 - 28 April 2021 - 9:30-12:00, 13:30-16:00
4. Day4 - 29 April 2021 - 9:30-12:00, 13:30-16:00