Tutorials

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
courses/smu/tutorials.txt · Last modified: 2018/05/22 10:29 by rysavpe1