CPSC 522 - AI 2
Schedule

Here is a tentative schedule. The dates for all topics in the future should be regarded as fiction. The readings are from the Textbook unless specified otherwise.

• Jan 4 - Intro to AI, Design space of AI, knowledge representation. Reading: chapter 1.
• Jan 6 - propositional logic, bottom-up and top-down proof procedures. Knowledge-level explanation and debugging. Reading: Sections 5.1, 5.2, 5.3. Assignment 0 (html, pdf, plain text) due
• Jan 11 - proof by contradiction and consistency-base diagnosis. complete knowledge assumption and negation as failure, assumption-based reasoning, default reasoning, evidential and causal reasoning. Readings: Sections 5.4-5.7.
• Jan 13 - First-order logic. Datalog, semantics, variables, proofs. Readings: Sections 12.1-12.5.
• Jan 18 - challenge problem - sustainable wastewater management - introduced by Brent Chamberlain. Assignment 1 (html, pdf; here is plumbingbuggy.ailog. We now have a solution) due on Jan 18.
• Jan 20 - challenge problem questions. Probability. Belief networks.
• Jan 25 - probabilistic inference.
• Jan 27 - - Assignment 2 proposed solutions. AIspace belief network tool. search-based probabilistic inference. Assignment 2 (html, pdf) due on Jan 27.
• Feb 1. search-based probabilistic inference. relational probabilistic models.
• Feb 3. Statistical Relational AI. (Cont.) Independent Choice Logic (from Probabilistic Inductive Logic Programming - free access from UBC). Learning belief networks. unsupervised learning. Reading: Section 11.1-11.2
• Feb 8. Discussion paper: Learning Probabilistic Relational Models.
• Feb 10. Learning belief networks (cont.) Assignment 3 (html, pdf now with a solution in html, pdf) due.
• Feb 15. Discussion paper: Markov Logic (from Probabilistic Inductive Logic Programming). Bayesian learning, beta distribution.
• Feb 17. Aggregation: naive Bayes, logistic regression, noisy-or, context-specific independence.
• Feb 22, 24. Midterm break.
• Feb 29. flexible representations and ontologies. Project proposal (pdf or html) Due (see also project ideas (pdf or html)).
• Mar 2 - Discussion paper: Koren, Y.; Bell, R.; Volinsky, C. Matrix Factorization Techniques for Recommender Systems (see also Koren, Y.; Bell, R. Advances in Collaborative Filtering).
• Mar 7 - Discussion paper: M. I. Jordan. Bayesian nonparametric learning: Expressive priors for intelligent systems, 2010.
• Mar 9 - Ontologies (cont.)
• Mar 14. Discussion paper: Semantically-Enabled Large-Scale Science Data Repositories, by Fox, P. et al, in 5th International Semantic Web Conference, 2006. Utilities. Decision network.
• Mar 16. Decision-theoretic planning and
• Mar 21. decision-theoretic planning (cont.)
• Mar 23, reinforcement learning. Tiny game.
• Mar 28. Discussion paper: K. Kersting, K. Driessens. Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains.
• Mar 30. reinforcement learning (cont.)
• Apr 4. Big picture.
• Tues Apr 10: 10:00am papers due, and distributed to reviewers
• Wed Apr 11: 2:45-4:15 half of project presentations
• Fri Apr 13: 11:00-12:30 remaining project presentations
• Monday Apr 16: noon. Paper reviews due and redistributed.
• Monday Apr 23: noon. Final papers due.

Last updated: 2012-03-23, David Poole