T | Date | Tutor | Deadline | Contents | Materials | |
---|---|---|---|---|---|---|
1 | 19.2. | PR | Introduction, Python environment | PDF entrance test sample solution | ||
2 | 26.2. | JB | PEAS agent model, environment properties, input/output types of machine learning models, demos | Slides | ||
3 | 5.3. | JB | Learning conjunctive and disjunctive concepts | tutorial3.pdfcv3_student.py cv3_referential_solution.py | ||
4 | 12.3. | JB | Assignment of the first student project | project1.zip smu_student_projects.pdf | ||
5 | 19.3. | OH | Bayesian Networks - semantics | tutorial1.zip | ||
6 | 26.3. | OH | Bayesian Networks - inference | tutorial2.zip | ||
2.4 | holiday | |||||
7 | 9.4. | OH | Assignment of the second student project | smu_project_2018.pdf project_2018.zip | ||
8 | 16.4. | MS | Inductive Logic Programming - learning from interpretations | smu_ilp_1.pdf ilptutorial1.rar | ||
9 | 23.4. | MS | Inductive Logic Programming - learning from entailment, ILP assignment | smu_ilp_2.pdf ilptutorial2nhw.rar | ||
10 | 30.4. | MS | RLGG, Propositionalization | smu_ilp_3.pdf muta.txt | ||
11 | 7.5. | PR | RL introduction, Assignment of the fourth project | rltutorial1.zip, slides, zip_HW (v 1.1.1), pdf_HW (v 1.1.1) | ||
12 | 14.5. | PR | Passive reinforcement learning agents, TD methods | zip, slides | ||
13 | 21.5. | PR | Problems by hand | Notes on convergence |