Schedule

BCSC 229: Schedule

Only students who are enrolled in the course may access the course materials online. You must be logged into Blackboard to download these files.

The following is a rough plan for the course. Depending on various factors, the course may cover materials at a slower or faster pace, and the course may omit some materials listed below and/or add other materials.

Note: Classes indicated with an asterisk (“*”) are Discussion Classes.

Class 1 (Jan 21): Organization / Introduction

Class 2 (Jan 23): Reasoning and Decision Making I

*Class 3 (Jan 28): Discussion Class: Reasoning and Decision Making II

  • Assigned reading: Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus, and Giroux. (pages 3-15 and 19-30)
  • Assigned brief report: Write a report on assigned reading

Class 4 (Jan 30): Introduction to Probability I

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 1)
  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 2)
  • Assigned reading: Russell, S. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (Third Edition). Upper Saddle River, NJ: Pearson Education. (pages 480-509)

Class 5 (Feb 4): Introduction to Probability II

  • Assigned reading: Russell, S. & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (Third Edition). Upper Saddle River, NJ: Pearson Education. (pages 480-509)

Class 6 (Feb 6): Probability Problem Set

  • Assigned reading: Read (and think about) the problems on the list of “Probability Problems” compiled by the instructor
  • Distribute Homework #1 (due at start of Class 10)

Class 7 (Feb 11): Statistical Inference

Class 8 (Feb 13): Implementing Probabilistic Processing Using Python

Class 9 (Feb 18): Our Expectations Influence Visual Perception

  • Assigned reading: Dawson, M. R. W. (1998). Understanding Cognitive Science. Malden, MA: Blackwell Publishers. (pages 243-270)

Class 10 (Feb 20): Building a Bayesian Model

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 3)
  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 4)

*Class 11 (Feb 25): Discussion Class: Sensory Cue Integration

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 5)
  • Assigned reading: Ernst, M. O. & Bülthoff, H. H. (2004). Merging the senses into a robust percept. Trends in Cognitive Sciences, 8, 162-169.
  • Assigned brief report: Write a report on Ernst & Bülthoff (2004)
  • Distribute Homework #2 (due at start of Class 17)

*Class 12 (Feb 27): Discussion Class: Visual Motion Perception

  • Assigned reading: Geisler, W. S. & Kersten, D. (2002). Illusions, perception, and Bayes. Nature Neuroscience, 5, 508-510.
  • Assigned reading: Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598-604.
  • Assigned brief report: Write a report on Weiss, Simoncelli, & Adelson (2002)

Class 13 (Mar 4): Review in preparation for Midterm Exam

Class 14 (Mar 6): Midterm Exam

Class 15 (Mar 18): Review of Midterm Exam

Class 16 (Mar 20): Bayesian Learning and Generalization

  • Assigned reading: Tenenbaum, J. B. & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629-640.

*Class 17 (Mar 25): Discussion Class: Visual Memory

  • Assigned reading: Hemmer, P. & Steyvers, M. (2009). A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1, 189-202.
  • Assigned reading: Griffiths, T. L. & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773.
  • Assigned brief report: Write a report on Griffiths & Tenenbaum (2006)

Class 18 (Mar 27): Introduction to Posterior Sampling

Class 19 (Apr 1): Same-Different and Other Causal Inference Problems I

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 10)
  • Distribute Homework #3 (due at start of Class 24)

Class 20 (Apr 3): Same-Different and Other Causal Inference Problems II

Class 21 (Apr 8): Visual Search

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 11)

Class 22 (Apr 10): Nuisance Variables and Ambiguity; Combining Inference with Utility

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 9)
  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 13)

*Class 23 (Apr 15): Discussion Class: Bayesian Response to Kahneman/Tversky I

  • Assigned reading: Sanborn, A. N. & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20, 883-893.
  • Assigned brief report: Write a report on Sanborn & Chater (2016)

Class 24 (Apr 17): Bayesian Response to Kahneman/Tversky II

  • Assigned reading: Bates, C. J. & Jacobs, R. A. (2019). Efficient data compression leads to categorical bias in perception and perceptual memory. Proceedings of the Forty-First Annual Conference of the Cognitive Science Society.

Class 25 (Apr 22): Bayesian Brain I

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 14)

Class 26 (Apr 24): Bayesian Brain II

  • Assigned reading: Ma, W. J., Körding, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action (Chapter 14)

Class 27 (Apr 29): Student presentations of final papers

Class 28 (May 1): Review for Final Exam