Lectures

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

The order of lectures is subject to change.

Date	W#	Who	Contents	Materials
19.02.2019	1	PP	AI, PR, learning and robotics. Decision tasks. Empirical learning.	Slides. Handouts.
26.02.2019	2	PP	Linear methods for classification and regression.	Slides. Handouts.
05.03.2019	3	RM	Non-linear models. Feature space straightening. Overfitting.	Slides. Handouts.
12.03.2019	4	PP	Nearest neighbors. Kernel functions, SVM. Decision trees.	Slides. Handouts.
19.03.2019	5	PP	Bagging. Adaboost. Random forests.	Slides. Handouts.
26.03.2019	6	PP	Neural networks. Basic models and methods, error backpropagation.	Slides. Handouts.
02.04.2019	7	PP	Deep learning. Convolutional and recurrent NNs.	Slides. Handouts.
09.04.2019	8	PP	Probabilistic graphical models. Bayesian networks.	Slides. Handouts.
16.04.2019	9	PP	Hidden Markov models.	Slides. Handouts.
23.04.2019	10	PP	Expectation-Maximization algorithm.	Slides. Handouts.
30.04.2019	11	RM	Planning. Planning problem representations. Planning methods.	Handouts
07.05.2019	12	RM	Constraint satisfaction problems.	Handouts
14.05.2019	13		No lecture. Schedule as on wednesday.
21.05.2019	14	RM	Scheduling. Local search.	Handouts