Lectures

List of competencies you should have after individual lectures. (Will be updated continuously.)

Work in prgress, update for the upcoming semester.

datum	č.t.	S/L	náplň
20.02.2018	1.	S
27.02.2018	2.	L
06.03.2018	3.	S
13.03.2018	4.	L
20.03.2018	5.	S
27.03.2018	6.	L
03.04.2018	7.	S
10.04.2018	8.	L
17.04.2018	9.	S
24.04.2018	10.	L
01.05.2018	11.	S	Svátek
08.05.2018	12.	L	Svátek
15.05.2018	13.	S
17.05.2018	13.	S	Výuka jako v úterý
22.05.2018	14.	L

Date	W#	Who	Contents	Materials
21.02.2017	1	RM	AI, PR, learning and robotics. Decision tasks. Empirical learning.	P01.LearningBayesStrategy.pdf
28.02.2017	2	PP	Linear methods for classification and regression.	Slides. Handouts.
07.03.2017	3	PP	Non-linear models. Feature space straightening. Overfitting.	Slides. Handouts.
14.03.2017	4	PP	Nearest neighbors. Kernel functions, SVM. Decision trees.	Slides. Handouts.
21.03.2017	5	PP	Bagging. Adaboost. Random forests.	Slides. Handouts.
28.03.2017	6	PP	Probabilistic graphical models. Bayesian networks.	Slides. Handouts.
04.04.2017	7	PP	Hidden Markov models.	Slides. Handouts.
11.04.2017	8	PP	Expectation-Maximization algorithm.	Slides. Handouts.
18.04.2017	9	RM	Planning. Planning problem representations. Planning methods.	Handouts
25.04.2017	10	RM	Scheduling. Local search.	Handouts
02.05.2017	11		No lecture - schedule as on even Monday
09.05.2017	12	RM	Constraint satisfaction problems.	Handouts
16.05.2017	13	PP	Neural networks. Basic models and methods, error backpropagation.	Slides. Handouts.
23.05.2017	14	PP	Deep learning. Convolutional and recurrent NNs.	Slides. Handouts.