AI Qualifying Exam Reading List Associated with CS 731 - ADVANCED ARTIFICIAL INTELLIGENCE

Topics

1. Probabilistic Graphical Models: Bayesian networks; inference by variable elimination, junction (clique) trees, Markov chain Monte Carlo; structure and parameter learning in Bayesian networks.
   [KOLL09, Chapters 3, 10, 11, 13, 19, 20]

2. First-order Logic and Inductive Logic Programming: Syntax and semantics of first-order logic; unification and resolution; refinement and least general generalization.
   [NILS00, Chapters 1-3; LAVR94, Chapters 2, 3, 7]

3. Statistical Relational Learning: Probabilistic relational models; Markov logic networks; view learning.
   [GETO01; RICH06; DAVI05]

References

[DAVI05]

Davis, J., Burnside, E., Dutra, I., Page, D., Ramakrishnan, R., Santos Costa, V. and Shavlik, J. (2005). View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI-05.

[GETO01]

Getoor, L., Friedman, N., Koller, D. and Pfeffer, A. (2001). Learning Probabilistic Relational Models. In Relational Data Mining, S. Dzeroski and N. Lavrac, Eds, Springer-Verlag.

[KOLL09]

Koller, D. and Friedman, N. (2009, to appear). Structured Probabilistic Models: Principles and Techniques. Hardcopy chapter handouts available from David Page, 6743 Medical Sciences Center, 265-6168.

[LAVR94]

Lavrac, N. and Dzeroski, S. (1994). Inductive Logic Programming: Techniques and Applications.

[NILS00]

Nilsson, U. and Malusynski, J. (2000). Logic, Programming and Prolog (2nd ed.).

[RICH06]

Richardson, M. and Domingos, P. (2006). Markov Logic Networks. Machine Learning, 62, pp. 107-136.