Probabilistic Models of the Visual Cortex:
Mon/Wed: 1:002:15 pm Fall 2013, Royce 162.
www.stat.ucla.edu/~yuille/Courses/
The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low, mid, and highlevel 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.
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 
Sep30 
Introduction 
Lecture1 
KerstenYuille 
2 
Oct2  Basic Architecture and
Limits to Vision 
Lecture2 
KerstenLimitsVision
Bialek 
3 
Oct7 
Neurons and Artificial
Neural Models 
Lecture3 
NeuronAsTwoLayers
NeuronsRealStimuli
Video:http://videolectures.net/nips2012_sejnowski_brain/ 
4 
Oct9 
Models of Neurons 
Lecture4 
MRFmodelsVision 
5 
Oct14 
EM learning and regression 
Lecture5 

6 
Oct16  Simple Cell Models of V1 
Lecture6 

7 
Oct21 
Nonclassicial receptive
fields 
TaiSingLeeExperiments 

8 
Oct23 
V2: Foregroundbackground 
Lecture8 

9 
Oct28  NeuroStereo_Shading 
Lecture9 
LeePNAS SammondsIN13 SammondsJN13 
10 
Oct30  Binocular Stereo 
Lecture10 

11 
Nov4  Motion_Part_1 
Lecture11 
MotionPhenon SlowSmoothL1L2 
12 
Nov6  Motion_Part_2 
Lecture12 

13 
Nov11  Veteran's Day 

14 
Nov13  Motion_Part_3 
Lecture13 
Bistability MaterialMotion PerceptualDiamond
Rokers 
15 
Nov18  Cue Coupling 
Lecture14 
CueCombination Knill IsBayesWorthTheEffort 
16 
Nov20  Deep Belief Networks (1) 
Guest Lecturer: Prof. Y.N.
Wu 

17 
Nov25 
Comp. Models of Ventral
Stream 
Lewis&Poggio 

18 
Nov27 
Compositional Models (1) 
LearningNotes HandNotes 

19 
Dec02 
Compositional Models (2) 
InferenceNotes 

20 
Dec04 
Deep Belief Networks (2) 
Guest Lecturer: Prof. Y.N.
Wu 