Bellow you can find scripts for running experiments or analyzing data, and other shared material.
If you are beginner in Machine Learning and you want to get a solid mathematical background in order to do Machine learning, this is a list of very good books that I used myself :
Mathematics for Machine Learning. by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong.
The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, is to provide the necessary mathematical skills to read those other books.
This course covers basic concepts in machine learning in high dimension, and the importance of regularization. We study in detail high-dimensional linear models regularized by the Euclidean norm, including ridge regression, ridge logistic regression and support vector machines. We then show how positive definite kernels allows to transform these linear models into rich nonlinear models, usable even for non-vectorial data such as strings and graphs, and convenient for integrating heterogeneous data.
Many estimation problems in statistical learning are formulated as optimization problems. These formulations allowed a separation between the analysis of the estimator’s performance and the development of problem-solving algorithms. Faced with large volumes of data, such separation is no longer effective and analysis must combine statistics and optimization. In this course, the classic statistical results on M-estimation and convex optimization will be presented in a unified way, showing the links between statistics and optimization. Emphasis will be placed on non-asymptotic analysis of stochastic approximation and stochastic gradient.
This course provides a unifying introduction to probabilistic modelling through the framework of graphical models, together with their associated learning and inference algorithms.