Lectures

AIMA and AIMA3 mean the 2nd and 3rd, respectively, edition of Russel, Norvig: Artificial Intelligence- Modern Approach.

W	Date	Who	Topic
1.	23.02.	P.Pošík	The relations of AI, ML, and robotics (handouts). Pattern recognition, Bayesian and non-Bayesian tasks (handouts).
2.	01.03.	P.Pošík	Learning (handouts). Linear regression (handouts).
3.	08.03.	P.Pošík	Linear classification models. Logistic regression. Perceptron. (Handouts) Optimal separating hyperplane. Basis expansion. Support vector machine. (Handouts)
4.	15.03.	P.Pošík	Decision trees (handouts).
5.	22.03.	P.Pošík	Bias-variance trade-off. Crossvalidation. Regularization. (Handouts.)
6.	29.03.	P.Pošík	Adaboost (handouts). Feature selection and extraction. (Handouts.)
7.	05.04.	P.Pošík	Theory of learning, problems of consistency, capacity, PAC. (Handouts.)
8.	12.04.	P.Pošík	Sequential pattern recognition, Wald's algorithm (Handouts). Artificial life, principles, algorithms, applications (handouts).
9.	19.04.	R.Mařík	HTN
10.	26.04.	R.Mařík	Scheduling
11.	03.05.	R.Mařík	Inteligent agents Decision theory
12.	10.05.	R.Mařík	Sequential decision making / Markov decision processes, value iteration, strategy iteration.
13.	17.05.	R.Mařík	Reinforcement learning: problem formulation, exploration vs. exploitation, bandit problems, sequential learning.
14.	24.05.	R.Mařík	Agent oriented programming: reactive planning, BDI architecture, AgentSpeak, Jason.