Computational learning theory

Język wykładowy Angielski
Semestr Zimowy
Status W ofercie
Opiekun Jan Otop
Liczba godzin 30 (wyk.) 30 (ćw.)
Rodzaj I2.T - teoria inf.
Polecany dla I roku No
Egzamin Yes
Tagi DS (Data Science)

Opis przedmiotu:

The lecture presents theory behind machine learning. We will cover the following topics: Introduction to PAC: 1. Introduction to PAC. Learnability of particular classes of concepts (DNF formulas, automata) 2. Occam's razor in the PAC context. PAC-reducibility. Which concepts are lernable: 3. Growth functions and the VC-dimension. 4. Rademacher's complexity and margin theory. Leraning methods: 5. Support Vector Machines. Kernels. 6. Boosting. Other learning frameworks: 7. Online learning. 8. Reinforcement learning. 9. Combination methods. The lecture and classes will be held remotely in case of lockdown in the autumn 2020.