Teaching

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

Courses

Abstract network of nodes and paths illustrating algorithms and dynamic programming.

M2 Data Science

Algorithms

Complexity, recursion, dynamic programming, and heuristics for NP-class problems.

Abstract classification boundary with two clusters of points and a neural-network pattern.

M1 Mathematics MINT

Introduction to supervised machine learning

Classification methods, tree-based models, neural networks, and practical coding.