course: JBI120

This course teaches concepts of linear algebra and probability theory necessary for AI applications, and some fundamental concepts in AI and machine learning, including the implementation of some concepts in Python. We begin with linear algebra, rehearsing fundamental structures like vector spaces, matrix decompositions, and spectral theory. We then transition into calculus, focusing on multivariate differentiation, Jacobian matrices, and convex optimization techniques. A significant portion is dedicated to probability theory. Finally, we explore complex probabilistic models such as Bayesian networks, Markov chains, and Hidden Markov models.

Details

Quartile:

Q1

Lecturers:

Arthur van Camp

Justyna Dąbrowska