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
20.02.2018	1	PP	AI, PR, learning and robotics. Decision tasks. Empirical learning.	Slides. Handouts.
27.02.2018	2	RM	Linear methods for classification and regression.	Slides. Handouts.
06.03.2018	3	PP	Non-linear models. Feature space straightening. Overfitting.	Slides. Handouts.
13.03.2018	4	PP	Nearest neighbors. Kernel functions, SVM. Decision trees.	Slides. Handouts.
20.03.2018	5	PP	Bagging. Adaboost. Random forests.	Slides. Handouts.
27.03.2018	6	PP	Neural networks. Basic models and methods, error backpropagation.	Slides. Handouts.
03.04.2018	7	PP	Deep learning. Convolutional and recurrent NNs.	Slides. Handouts.
10.04.2018	8	PP	Probabilistic graphical models. Bayesian networks.	Slides. Handouts.
17.04.2018	9	PP	Hidden Markov models.	Slides. Handouts.
24.04.2018	10	PP	Expectation-Maximization algorithm.	Slides. Handouts.
01.05.2018	11		No lecture. Public holiday.
08.05.2018	12		No lecture. Public holiday.
15.05.2018	13	RM	Planning. Planning problem representations. Planning methods.	Handouts
17.05.2018	13	RM	Schedule as on Tuesday. Constraint satisfaction problems.	Handouts
22.05.2018	14	RM	Scheduling. Local search.	Handouts