CPSC 532 - Topics in AI:
Statistical Relational Artificial Intelligence

Here is a tentative schedule. The dates for all topics in the future should be regarded as fiction.

• Jan 4 - Intro to Statistical Relational AI
• Jan 6 - Assignment 0 due
• Jan 9 - Background: machine learning, probability, graphical models.
• Jan 11 - Assignment 1, part A due
• Jan 16 - discussion paper: Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553): 452-459. http://dx.doi.org/10.1038/nature14541
• Jan 23 - discussion paper: Section 3.2 of Textbook.
• Jan 30 - Koren, Y., Bell, R. and Volinsky, C., Matrix Factorization Techniques for Recommender Systems, IEEE Computer 2009.
• Feb 6 - Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann and Jude Shavlik. Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case, Special issue of Machine Learning Journal (MLJ), Volume 86, Number 1, 25-56, 2012. You can find the code and the tutorial at http://www.indiana.edu/~iustarai/software.html.
• Feb 15 - Marlin, B.M., Zemel, R.S., Roweis, S.T., and Slaney, M. (2011). Recommender systems, missing data and statistical model estimation. In IJCAI, pp. 2686–2691.
• Feb 27: Note: class starts after recruiting talk. Representation issues (cont.).
• Mar 1: Part 1: Discussion on our ongoing problem. Part 2: discussion paper: Incremental Knowledge Base Construction Using DeepDive Sen Wu, Ce Zhang, Christopher De Sa, Jaeho Shin, Feiran Wang, and C. Re. VLDB. 2015.
• Mar 20: learning graphical models learning Bayes nets, EM.
• Mar 22: Lecture 1 flexible representations, semantic networks, frames, and property inheritance.
Ontologies, data and probabilistic hypotheses

Last updated: 2016-12-29, David Poole