Schedule:

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.

 

Day 1

Day 2

Day 3

Day 4

Day 5

 

Suggested readings (***primary readings, focusing on technical material, where you will benefit most from reading in advance):

Day 1:

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.

 

Day 2:

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.

 

Day 3:

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.

 

Day 4:

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.

Day 5:

***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