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 | |