courses:b4m36smu:tutorials [CourseWare Wiki]

T	Date	Tutor	Deadline	Contents	Materials
1	4.10.	PR		Introduction, Python environment	PDF
2	27.2.	JB		PEAS agent model, environment properties, input/output types of machine learning models, demos	solution to entrance test Slides
3	6.3.	JB		Learning conjunctive and disjunctive concepts	tutorial3.pdf cv3_student.py cv3_referential_solution.py
4	13.3.	JB		Assignment of the first student project	project1.zip smu_student_projects.pdf
5	20.3.	OH		Bayesian Networks - semantics	tutorial1.zip
6	27.3.	OH		Bayesian Networks - inference	tutorial2.zip
7	3.4.	OH		Assignment of the second student project	smu_project_v1.01.pdf project.zip
8	10.4.	MS		Inductive Logic Programming - learning from interpretations	ilp1.pdf ilptutorial1.zip
9	24.4.	MS	9. 5. 2017	Inductive Logic Programming - learning from clauses	ilp2nassignment.pdf ilpassignment_1.0.2.zip
10	2.5.	MS		ILP, Q&A	ilp3_v1.1.pdf
11	11.5.	PR		Reinforcement learning demos and introduction	rltutorial1.zip, slides
12	15.5.	PR	06/02/2017 (5am)	ILP example, Assignment of the fourth project	zip (v 1.0.3), pdf (v 1.0.3)
13	22.5.	PR		Passive reinforcement learning agents, TD methods	zip, slides