CS 540 - Introduction to Artificial Intelligence Section 1 Fall 2019

CS 540 Reading Assignments

• 12/10: Chapter 18.10
• 12/3: Chapter 15.1 - 15.3, 23.5, and "Markov models and hidden Markov models: A brief tutorial" by E. Fosler-Lussier, Technical Report TR-98-041, Int. Computer Science Institute, University of California, Berkeley, 1998
• 11/26: Chapter 18.9
  Optional: "Support vector machines" edited by M. Hearst, IEEE Intelligent Systems, July/August 1998, 18-28
• 11/19: Chapter 14.1, 14.2, and 14.4
  Optional: "Bayesian networks without tears" by E. Charniak, AI Magazine, Winter 1991, 50-63
• 11/14: Chapter 13
  Optional articles about misuse of probability for making inferences:
  • "Inferences about testosterone abuse among athletes" by D. Berry and L. Chastain, Chance 17(2), 2004, 8-11
  • "Justice flunks math" by L. Schneps and C. Colmez, New York Times, March 26, 2013
• 11/7: Deep learning by Y. LeCun, Y. Bengio and G. Hinton, Nature 521, 2015, 436-444
• 10/31: Chapter 18.6.3, 18.6.4, 18.7
• 10/29: Chapter 6.1 - 6.4
• 10/22: Chapter 18.8.1, 4.1.4
• 10/8: Chapter 18.2 - 18.4
  Optional: Decision Tree demo
• 10/3: Chapter 18.1, 18.8.1, and Chapter 1: Introduction to statistical machine learning by X. Zhu and A. Goldberg, from Introduction to Semi-Supervised Learning, Morgan and Claypool Publishers, 2009.
  Optional: Hierarchical Clustering demo
  Optional: K-Means Clustering demo
  Optional: k-NN demo
• 9/24: Chapter 5.1 - 5.3, 5.5
  Optional: Minimax/Alpha-Beta interactive demo
  Optional: Alpha-Beta pruning algorithm example
• 9/19: Chapter 4.1
• 9/17: Chapter 3.5 and 3.6
  Optional: Search methods demo
• 9/10: Chapter 3.1 - 3.4
• 9/5: Chapters 1 and 2 (focus on Sections 1.1, 2.1, 2.2 and 2.3; skim other sections) in the textbook (Artificial Intelligence: A Modern Approach, 3rd ed., S. Russell and P. Norvig, Prentice Hall, 2010)