The second edition of the course „Mathematics for Understanding Artificial Intelligence” covers the mathematical foundations necessary for data science, machine learning, and artificial intelligence. The program is free and combines a rigorous approach with applications relevant to these fields.
Topics include probability theory: random variables and vectors, multivariate distributions, principal component analysis, Mahalanobis distance, laws of large numbers, Monte Carlo simulations, the central limit theorem, and Markov chains. The mathematical statistics component covers estimation, confidence intervals, hypothesis testing, and regression.
The introduction to optimization covers the gradient and conjugate gradient methods, stochastic and accelerated variants, the AdaGrad algorithms, RMSProp, Adam, and NAdam algorithms, backpropagation of error, the Newton method, linear programming, the Simplex method, and constrained optimization. Successful completion of the course is recognized with a certificate of participation issued by the institution.
Details
- Organizer
- The PoliMath Mathematics Lab, led by Prof. Alexandru Negrescu.
- Activity Schedule
- Effective October 22, 2026.
- Public
- POLITEHNICA Bucharest students who are in at least their second year of study.