Stat 271

Probabilistic Models of the Visual Cortex:
 
Mon/Wed: 1:00-2:15 pm Fall 2013, Royce 162.
 
www.stat.ucla.edu/~yuille/Courses/
 

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Course Description

The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning.

Reading Material

• Course notes (available on line)
• Theortical Neuroscience: P. Dayan and L.F. Abbott. MIT Press 2001. This gives more detailed background on the neuroscience.
  
• Instructor: Prof. Alan Yuille, yuille@stat.ucla.edu, 310-267-5383, office Math Sciences 8967.
• Office Hours: Wednesday 4.00-5.00 pm (8967 Math Sciences), or by appointment (email).
• Syllabus.
  

Grading Plan: 4 homework assignments, 1 final project.
Homework 1: homework 1 Due
Homework 2: homework 2 Due
Homework 3: homework 3 Due
Homework 4: homework 4 Due

Tentative Schedule.

Lecture	Date	Topics	Reading Materials	Handouts
1	Sep-30	Introduction	Lecture1	KerstenYuille
2	Oct-2	Basic Architecture and Limits to Vision	Lecture2	KerstenLimitsVision Bialek
3	Oct-7	Neurons and Artificial Neural Models	Lecture3	NeuronAsTwoLayers NeuronsRealStimuli Video:http://videolectures.net/nips2012_sejnowski_brain/
4	Oct-9	Models of Neurons	Lecture4	MRFmodelsVision
5	Oct-14	EM learning and regression	Lecture5
6	Oct-16	Simple Cell Models of V1	Lecture6
7	Oct-21	Non-classicial receptive fields		TaiSingLeeExperiments
8	Oct-23	V2: Foreground-background	Lecture8
9	Oct-28	Neuro-Stereo_Shading	Lecture9	LeePNAS SammondsIN13 SammondsJN13
10	Oct-30	Binocular Stereo	Lecture10
11	Nov-4	Motion_Part_1	Lecture11	MotionPhenon SlowSmoothL1L2
12	Nov-6	Motion_Part_2	Lecture12
13	Nov-11	Veteran's Day
14	Nov-13	Motion_Part_3	Lecture13	Bistability MaterialMotion PerceptualDiamond Rokers
15	Nov-18	Cue Coupling	Lecture14	CueCombination Knill IsBayesWorthTheEffort
16	Nov-20	Deep Belief Networks (1)		Guest Lecturer: Prof. Y.N. Wu
17	Nov-25	Comp. Models of Ventral Stream		Lewis&Poggio
18	Nov-27	Compositional Models (1)		LearningNotes HandNotes
19	Dec-02	Compositional Models (2)		InferenceNotes
20	Dec-04	Deep Belief Networks (2)		Guest Lecturer: Prof. Y.N. Wu