Teaching

My teaching covers basic statistics, probability theory, statistical learning, R/Rcpp and Python programming, algorithm, and machine learning. Course materials are available through the relevant university e-learning platforms (ecampus).

Courses

• M2 Data Science — Algorithms: complexity, recursion, dynamic programming and heuristics for NP-class problems.

• M1 Mathematics MINT — Introduction to supervised machine learning: Classification LDA, QDA, logistic regression, SVM, decision trees, random forests, gradient boosting, neural networks. Code and theory.

Updated course materials for both courses will be made available on this website soon.