Stat 271

Probabilistic Models of the Visual Cortex:
 
Tues/Thurs: 12:30-1:45 pm Fall 2015, Franz 5264.
 
www.stat.ucla.edu/~yuille/Courses/
 

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

Grading Plan: 4 homework assignments, 1 final project.
Homework 1:
Due 27/Oct/2015                                                                                                                                                                                                                                                                                                                            Homework 2: Due 17/Nov/2015
Homework 3. Due 15/Dec/2015

Tentative Schedule.

Lecture Date Topics

Reading Materials

Handouts
1

Sept-24

Introduction; Guest Lecturer. Dr. Vittal Premachandran
IntroLecture

2
Sept-29
A Walk through the Mammalian Visual System. Prof. C. Reid (Allen Institute) online
Lecture2

3

Oct-01

The Retina and Linear Models of Simplified Cells
RetinaIntroduction
LinearModelsSimplifiedCells
4

Oct-06

Introduction to V1, Simple Cells in V1, Intro to Sparsity
Lecture4  SimpleCellsCortex
LinearFilters
LinearFilterKokkinos
5

Oct-08

Sparsity, Matched Filters, Hebbian Learning, Regression
BasisFunctionsSparsity HebbsRegression
SparsityStatistics  CurtisBaker
6
Oct-13 Filters Methods for Binoculare Staero and Motion
StereoMotion

7

Oct-15

Probabilites, Decision Theory, and Context
ProbabilitiesDecisions
ContextMarkov
8

Oct-20

Boltzmann Machines
BoltzmannMachines
Online Lectures by G. Hinton
9
Oct-22 Probabilities and Context Review
ContextExamples
Previous Material and YuilleKersten Chapter
10
Oct-27
Context and Attention
AttentionHierarchicalSegmentation
LeeGroupExperimentsSummary
11
Oct 29
Cue Coupling: Directed Graphical Models.
BasicCueCouping CausalCueCoupling
CausalDivisive
12
Nov 3
Motion Perception and Prediction
MotionPhenomena   MotionModels
Kalman1 Kalman2 KalmanMotionTracking
13
Nov 5
Perceptrons and Multilayer Perceptrons Perceptrons  MultilayerPerceptronsRegression

14
Nov 10
Deep Networks:  Guest Lecturer. Dr. Vittal Premachandran DeepNetworksIntro AlexNet
DeepNetsAndRealNeurons
15
Nov 12
Deep Network Example: With context HumanPose SemanticSegmentation
ModelsVentralStream
16
Nov 17
Compositional Hierarchical Models CompositionComplexity CompositionLearning
MathsComposition
17
Nov 19
Visual Grammars
ParsingObjects BigPicture

18
Nov 24
Reinforcement Learning ReinforcementLearning
ReinforcementNotes
19
Dec 1
Recurrent Neural Networks and LSTMs; Guest Lecturer Junhua Mao
CriticalReviewRNNsLSTMs

20
Dec 3
Review of Course