AIMA is short for the recommended course text: Russell, Stuart, J., and Peter Norvig. *Artificial Intelligence: A Modern Approach.* 2nd ed. Upper Saddle River, NJ: Prentice Hall/Pearson Education, 2003. ISBN: 0137903952.

Lec # | Topics | readings |
---|---|---|

1 | Introduction |
Goodman, Nelson. “The New Riddle of Induction.” In Fodor, Chomsky. “On the Impossibility of Acquiring more Powerful Structures,” and “The Inductivist Fallacy (including discussion).” In ———. “The Inductivist Fallacy.” In
Fodor, J. A., M. F. Garrett, E. C. T. Walker, and C. H. Parkes. “Against Definitions.” Laurence, Stephen, and Eric Margolis. “Radical Concept Nativism.” |

2 | Foundations of Inductive Learning |
AIMA. Sections 18.1-2, 18.5, and 19.1-2. Berwick, R. C. “Learning From Positive-only Examples: The Subset Principle and Three Case Studies.” Bruner, Jerome S., Jacqueline J. Goodnow, and George Austin. Mitchell, Thomas M.
Feldman, J. “Minimization of Boolean Complexity in Human Concept Learning.” Kearns, Michael J., and Umesh V. Vasirani. Winston, P. H., ed. “Learning Structural Descriptions from Examples.” In |

3 | Knowledge Representation: Spaces, Trees, Features |
Shepard, R. N. “Multidimensional Scaling, Tree-fitting, and Clustering.” Landaues, T. K., and S. T. Dumais. “A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of the Acquisition, Induction, and Representation of Knowledge.” Goldstone, R. L, and J. Son. “Similarity.” In |

4 | Knowledge Representation: Language and Logic 1 |
AIMA. Sections 22.1-22.2, “Basics of Formal Grammars,” and Section 22.8, “Grammar Induction.” ———. Sections 8.1-8.3, “First Order Logic: See 7.1-7.4 if necessary for background on logic,” Section 10.6, “Using Logic to Represent Category Relations,” and Section 19.5, “Learning New Concepts in Logic: An Answer to Fodor’s Challenge?” Chomsky, Noam. Markman, Arthur.
Nowak, M. A., N. L. Komarova, and P. Niyogi. “Computational and Evolutionary Aspects of Language.” Gentner, D., and A. B. Markman. “Structural Alignment in Analogy and Similarity.” |

5 | Knowledge Representation: Language and Logic 2 |
At least one of the following three pairs of papers: 1. Rosch, E. “Principles of Categorization.” In 2. Armstrong S. L., L. R. Gleitman, and H. Gleitman. “What Some Concepts Might Not Be.” 1. Pinker, S. “Why the Child Holded the Baby Rabbits: A Case Study in Language Acquisition.” In 2. Rumelhart, D. E., and J. L. McClelland, eds. “On Learning the Past Tenses of English Verbs.” Chapter 18 in 1. Collins, A. M., and M. R. Quillian. “Retrieval Time from Semantic Memory.” 2. McClelland, and Rogers. “The Parallel Distributed Processing Approach to Semantic Cognition.”
Rumelhart, D. E., and J. L. McClelland, eds. “Schemata and Sequential Thought Processes in PDP Models.” Chapter 14 in Goldstone, R. L., and A. Kersten. “Concepts and Categories.” In Paccanaro, A., and G. E. Hinton. “Learning Distributed Representations of Concepts Using Linear Relational Embedding.” Technical Report: GCNU TR 2000-002, March 2000. |

6 | Knowledge Representation: Great Debates 1 |
AIMA. Chapter 13. Jeffreys, and Berger. “Bayesian Occam’s Razor.” Tversky, A., and D. Kahneman. “Judgement under Uncertainty: Heuristics and Biases.”
Sivia. |

7 | Knowledge Representation: Great Debates 2 |
AIMA. Sections 14.1-14.3, and 14.5. Charniak. “Bayesian Networks without Tears.” McClelland, J. L., D. E. Rumelhart, andG. Hinton. “The Appeal of Parallel Distributed Processing.” In Johnson-Laird, P. N., and Fabien Savary. “Illusory Inferences about Probabilities.” |

8 | Basic Bayesian Inference |
AIMA. Sections 20-20.2. Gelman, Carlin, Stern, and Rubin. “Hierarchical Models.” Chapter 5 in Griffiths, T. L., and M. Steyvers. “A Probabilistic Approach to Semantic Representation.” In Review: Goodman, Nelson. “The New Riddle of Induction.” In |

9 | Graphical Models and Bayes Nets |
For review: AIMA. Section 19.2. Mitchell, Thomas M. “Bayesian Learning.” Chapter 6 in Tenenbaum, J. B. “Rules and Similarity in Concept Learning.” In Posner, and Keele. “On the Genesis of Abstract Ideas.” If necessary for background: Bishop, C. M. “Bayesian Classification.” |

10 | Simple Bayesian Learning 1 |
AIMA. Section 20.3. Fried, and Holyoak. “Induction of Category Distributions: A Framework for Classification Learning.” Ghahramani, Z., and M. I. Jordan. “Learning from Incomplete Data.” MIT Center for Biological and Computational Learning Technical Report 108 (1994).
Zhu, Xiaojin, Zoubin Ghahramani, and John Lafferty. “Semi-supervised Learning using Gaussian Fields and Harmonic Functions.” Nigam, Kamal, Andrew McCallum, Sebastian Thrun, and Tom Mitchell. “Text Classification from Labeled and Unlabeled Documents using EM.” Seeger, M. “Learning with Labeled and Unlabeled Data.” University of Edinburgh Institute for Adaptive and Neural Computation Technical Report (2001). |

11 | Simple Bayesian Learning 2 |
AIMA. Sections 20.4-5. Nosofsky, R. M. “Optimal Performance and Exemplar Models of Classification.” In Kruschke, J. K. “Human Category Learning: Implications for Backpropagation Models.”
B. W. Silverman. “Maximum Penalized Likelihood Estimators.” In Hinton, G. E., P. Dayan, B. J. Frey, and R. M. Neal. “The Wake-sleep Algorithm for Unsupervised Neural Networks.” |

12 | Probabilistic Models for Concept Learning and Categorization 1 |
Anderson, J. R. “The Adaptive Nature of Human Categorization.” Smyth, P. “Model Selection for Probabilistic Clustering using Cross-validated Likelihood.” MacKay, D. J. C. “Probable Networks and Plausible Predictions - A Review of Practical Bayesian Methods for Supervised Neural Networks.” |

13 | Probabilistic Models for Concept Learning and Categorization 2 | |

14 | Unsupervised and Semi-supervised Learning |
Murphy, Gregory L., and Douglas L. Medin. “The Role of Theories in Conceptual Coherence.” Chapter 19 in Gelman, Susan A.
Courville, Aaron C., Nathaniel D. Daw and David S. Touretzky. “Similarity and Discrimination in Classical Conditioning: A Latent Variable Account.” Rehder, Bob. “Essentialism as a Generative Theory of Classification.” To appear in Gopnik, A., and L. Schulz (Eds.) |

15 | Non-parametric Classification: Exemplar Models and Neural Networks 1 | |

16 | Non-parametric Classification: Exemplar Models and Neural Networks 2 |
Pearl, Judea. Gregory F. Cooper. “An Overview of the Representation and Discovery of Causal Relationships using Bayesian Networks.” In Gopnik, Alison, and Laura Schulz. “Mechanisms of Theory Formation in Young Children.” |

17 | Controlling Complexity and Occam’s Razor 1 |
Wellman, Henry M., and Susan A. Gelman. “Cognitive Development: Foundational Theories of Core Domains.” Tenenbaum J. B., and Griffiths. “The Place of Intuitive Theories in Rational Causal Inference.” To appear in Gopnik, A., and L. Schulz (Eds.) Charles Kemp, Thomas L. Griffiths, and Joshua B. Tenenbaum.
Rehder, Bob. “Essentialism as a Generative Theory of Classification_.”_ To appear in Gopnik, A., and L. Schulz (Eds.) |

18 | Controlling Complexity and Occam’s Razor 2 |
AIMA. 14.6. Milch, Brian, Bhaskara Marthi, and Stuart Russell. “BLOG: Relational Modeling with Unknown Objects.” Workshop on Statistical Relational Learning and Its Connections to Other Fields. |

19 | Intuitive Biology and the Role of Theories |
Osherson, Daniel N., O. Wilkie, E. E. Smith, A. Lopez, and E. Shafir. “Category-Based Induction.” Atran, Scott. “Classifying Nature Across Cultures.” In Kemp, C., and J. B. Tenenbaum. “Theory-based Induction.” In
Kemp, C., T. L. Griffiths, S. Stromsten, and J. B. Tenenbaum. “Semi-supervised Learning with Trees.” In |

20 | Learning Domain Structures 1 |
Keil, Frank C. “Contraints on Knowledge and Cognitive Development.” McClelland, and Rogers. “The Parallel Distributed Processing Approach to Semantic Cognition.” Kemp, Charles, Amy Perfors, and Joshua B. Tenenbaum.
Geman, Stuart, and E. Bienenstock. “Neural Networks and the Bias/Variance Dilemma.” |

21 | Learning Domain Structures 2 |
Scholl, Brian J.,and Patrice D. “Perceptual Causality and Animacy.” Feldman, Jacob, and Patrice D. Tremoulet. |

22 | Causal Learning | |

23 | Causal Theories 1 | |

24 | Causal Theories 2 | |

25 | Project Presentations |