Monday, May 19, 5:00-7:00, Swift 107
Tuesday, May 20, 12:00-2:00, Jacobs Center 102. [Off the main lobby, Kellogg.]
Wednesday, May 21, 3:00-6:00, Jacobs Center 102. [Off the main lobby, Kellogg.]
Thursday, May 22, 12:00-2:00, Swift 210
Friday, May 23, 1:30-3:00, Swift 231
Lecture presentations: the following are links to the PowerPoint presentations Josh Tenenbaum used for this tutorial. Clicking on a link will open PowerPoint in your browser, so if you don't want to open PowerPoint right now, right-click to download to disk.
Suggested readings (***primary readings, focusing on technical material, where you will benefit most from reading in advance):
Sivia, D. S. (1996). Data Analysis: A Bayesian Tutorial. Oxford University Press. Pages 1-22.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco: Morgan Kaufmann. 1-75.
[Also, the beginning of Pearl (2000), below, would be useful.]
Lecture notes from Day 1. Note: this link opens a PowerPoint presentation in your web browser. If you don't want to open PowerPoint now, right-click the link to save to disk. If you want to view the presentation in PowerPoint without using the browser, right-click to save to disk, and then open the saved file in PowerPoint.
Basic probability handout This was distributed on paper in the workshop.
R. N. Shepard (1987). Towards a universal theory of generalization for psychological science. Science, 237, 1317-1323.
A. Tversky (1977). Features of similarity. Psychological Review, 84, 327-352. Warning: this document will print unreadably (as a mirror image) unless you choose the "Print as Image" option in the print dialogue box when you print from Adobe Acrobat.
***J. B. Tenenbaum and T. L. Griffiths (2001). Tenenbaum, J.B., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629-640. Note: this link provides the article cited, plus peer commentary on the article, and the other work in the issue to do with Roger Shepard. The first page is a table showing who comments on what, but it is misleadingly labeled (editing mistake at publication).
***Tenenbaum, J.B., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629-640 Note: this is just the Tenenbaum & Griffiths article without the rest of the BBS issue.
***J. B. Tenenbaum (2000).Rules and similarity in concept learning. Advances in Neural Information Processing Systems 12, 59-65.
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., and Shafir, E. (1990). Category-based induction. Psychological Review, 97, 185-200.
***Kemp, C. S., & Tenenbaum, J. B. (2003).Theory-based induction. Submitted to Cogsci 2003.
***Tenenbaum, J. B. & Xu, F. (2000). Word learning as Bayesian inference. Cogsci 2000.
Shanks, D. R. (1995). Is human learning rational? The Quarterly Journal of Experimental Psychology, 48A(2), 257-279.
***Buehner, M. J., & Cheng, P.W. (1997). Causal induction: The power PC theory versus the Rescorla-Wagner model. In M. G. Shafto & P. Langley (Eds.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (pp. 55-60). Hillsdale, NJ: Lawrence Erlbaum Associates.
***Pearl, J. (2000). Causality: models, reasoning, and inference. New York: Cambridge University Press, 1-40.
***Cooper, G. F. (1999). An Overview of Representation and Discovery of Causal Relationships Using Bayesian Networks. In Glymour, C. & Cooper, G.F. (Eds.), Computation, Causation, and Discovery. Cambridge: AAAI Press, 3-62.
***Griffiths, T. L. & Tenenbaum, J. B. (2000).Structure learning in human causal induction. Advances in Neural Information Processing Systems 13.
***Gopnik, A., Glymour, C., Sobel, D., Schulz, L. E., Kushnir, T., & Danks, D. (in press). ). A theory of causal learning in children: Causal maps and Bayes nets. To appear in Psychological Review.
***Tenenbaum, J. B. & Griffiths, T. L. (2002). Theory-based causal inference. Advances in Neural Information Processing Systems 15.
Tenenbaum, J. B. & S. Niyogi (2003). Learning causal laws. Submitted to Cogsci 2003.
***primary readings, focusing on technical material, where you will benefit most from reading in advance