In this course, we will study the biological and psychological data of biological perceptual systems, mostly the visual system, in depth, and then apply computational thinking to investigate the principles and mechanisms underlying natural perception. You will learn how to reason scientifically and computationally about problems and issues in perception, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. The course is targeted to both neuroscience and psychology students who are interested in learning computational thinking, as well as computer science and engineering students who are interested in learning more about the neural and computational basis of perception. Prerequisites: First year college calculus, differential equations, linear algebra, basic probability theory and statistical inference, and programming experience (Matlab) are desirable.
Instructors | Office (Office hours) | Email (Phone) |
---|---|---|
Tai Sing Lee (Professor) | zoom: | tai@cnbc.cmu.edu (412-268-1060) |
Andrew Luo (TA) | zoom: | afluo@andrew.cmu.edu |
Evaluation | % of Grade |
---|---|
Assignments | 65 |
Midterm | 10 |
Final Exam | 15 |
Class Attendance and Participation | 10 |
Term Paper or project (optional for 387) | replacement grade for one homework or mid-term |
Evaluation | % of Grade |
---|---|
Assignments | 65 |
Journal Club | 30 |
Term Project | 30 |
Exams (25) + class participation | 30 |
Evaluation | % of Grade |
---|---|
Assignments | 40/65 |
Journal Club | 30 |
Term Project/Paper | 30 |
Exams (25) + class participation (5) | 30 |
Date | Lecture Topic | Relevant Readings | Assignments | |
---|---|---|---|---|
SENSORY CODING | ||||
M 8/31 | 1. Introduction | ch. 1, Marr | ||
W 9/2 | 2. Computational Approach | ch 1 Marr, ch1, FS | ||
M 9/7 | Label Day (no class) | |||
W 9/9 | 3. Retina | FS ch 6 and ch 3, Gollisch and Meister | Homework 1 | |
M 9/14 | 4. Pyramid | Burt and Adelson | ||
W 9/16 | 5. Frequency Analysis | FS Ch 4 and ch 5 | ||
M 9/21 | 6. Representation | FS ch 9, Shlens |   | |
W 9/23 | 7. Source separation | Fodiak, Olshausen | Homework 2 | |
PERCEPTUAL INFERENCE | ||||
M 9/28 | 8. Lightness and color | ch 16, Land, Horn, Morel | ||
W 9/30 | 9. Intrinsic images and Retinex | ch 17, Adelson, Weiss, Freeman | ||
M 10/5 | 10. Perceptual Systems | ch 10 (brain maps), Van Essen | ||
W 10/7 | 11. Multi-sensory Integration | ch 20 | Homework 3; | |
M 10/12 | 12. Bayesian inference | ch 13 (inference) | ||
W 10/14 | Midterm | |||
M 10/19 | 13. Perceptual Organization | ch 7 | Project Proposal due | |
W 10/21 | 14. Features and Contours | Geisler, Elders | ||
M 10/26 | 15. Texture Perception | Julesz | ||
W 10/28 | 16. Texture Metamer | Simoncelli | Homework 4 | |
M 11/2 | 17. Source Separation | Bells and Sejnowski, Olshausen and Field | ||
W 11/4 | 18. Depth and Stereo | Poggio | ||
OBJECT AND SCENES | ||||
M 11/9 | 19. Figure-Ground Perception | ch 7 | ||
W 11/11 | 20. Hierarchy | Fukushima, Van Essen | Homework 5 | |
M 11/16 | 21. Deep Network | LeCun, Hinton | ||
W 11/18 | 22. Analysis by Synthesis | Tenenbaum | ||
M 11/23 | 23. Recurrent Feedback | Lee and Mumford, Hinton | ||
W 11/25 | Thanksgiving break | |||
M 11/30 | 24. Predictive Coding | Rao and Ballard, Lotter and Cox | HW 5 due | |
W 12/2 | 25. Motion Perception | Weiss, Adelson | ||
M 12/7 | 26. Scene Analysis | Oliva and Torralba | ||
W 12/9 | 27. Object and face perception | Tenenbaum, Tsao and Freiwald | Project and Term Paper Due | |
X 12/18 | Final Exam and Presentations |