Office hours by appointment
Lectures:
Tue/Thu 10:30 - 11:50, Porter Hall A19
How brains do it? The perceptual capabilities of even the simplest biological organisms are far beyond what we can achieve with machines. Whether you look at sensitivity, robustness, or sheer perceptual power, perception in biology just works, and works in complex, ever changing environments, and can pick up the most subtle sensory patterns. Is it the neural hardware? Does biology solve funamentally different problems? What can we learn from biological systems and human perception?
This course teaches advanced aspects of perception and scene analysis in both the visual and auditory modalities, concentrating on those aspects that allow us and animals to behave in natural, complex environments. In this course, you will learn how to reason scientifically about problems and issues in perception and scene analysis, how to extract the essential computational properties of those abstract ideas, and finally how to convert these into explicit mathematical models and computational algorithms. In the process, you will cover a wide range of literature that provide a very different perspectives on problems and properties of natural perception.
• syllabus
• class topics, readings, and references
Schedule:
This is subject to change and is updated here.
Date |
Topics |
Handouts |
Assignments & Notes |
1/15 |
Course overview and general issues [slides] |
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Read both of these overview articles on perception science. |
1/17 |
Sound localization: Part 1 [slides] |
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These handouts are provided for background. Use them as reference material for the auditory lectures and homework assignments. |
1/22 |
Part 2 [slides] |
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Read the handout for class discussion on Thursday 1/24. HW1 out. [pdf] [materials] For background and to get started with matlab you can go through the tutorial on filtering, convolution, and the Fourier domain using MATLAB. [pdf] [code] |
1/24 |
Part 3 [slides] |
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1/29 |
Bayesian inference and modeling [slides] |
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A tutorial on classical and Bayesian inference [pdf] |
1/31 |
Part 2 (on board) |
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HW1 due; HW2 out. [pdf] [materials] |
2/5 |
Auditory coding : Part 1 [slides] |
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2/7 |
Part 2 [slides] |
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2/12 |
Visual coding: Part 1 [slides] |
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2/14 |
NO CLASS |
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HW2 due; HW3 out. [pdf] [materials] |
2/19 |
Part 2 [slides] |
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2/21 |
Part 3 [slides] |
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2/26 |
Visual motion: Part 1 [slides] |
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HW3 due; HW4 out. [pdf] [materials] |
2/28 |
Part 2 [slides] |
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3/4 |
Perceptual inference: Part 1 [slides] |
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3/6 |
Part 2 [slides] |
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HW4 due |
3/10-14 |
(no class - spring break) |
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3/18 |
Visual structure, representations of shape and surfaces: Part 1 [slides] |
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3/20 |
Part 1 (cont'd) |
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3/25 |
(no class) |
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3/27 |
Visual structure: Part 2 [slides] |
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grad project proposals due |
4/1 |
Perceptual constancy: Part 1 [slides] |
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4/3 |
|
HW5 [pdf] [materials] |
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4/8 |
Auditory structure Part 1 [slides] |
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4/10 |
Part 2 [slides] |
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4/15 |
Auditory scene analysis [slides] |
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4/17 |
(no class; spring carnival) |
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4/22 |
Eye movements, visual search, & attention [slides] |
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HW5 due |
4/24 |
Visual scene analysis |
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4/29 |
Perceptual organization |
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5/1 |
Project presentations |
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project presentations due |
5/2 |
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grad projects due |