Although there is no official class textbook, we will make some use of some material from "Natural Image Statistics: A Probabilistic Approach to Early Computational Vision" by Hyvarinen, Hurri, Hoyer (Springer 2009). 
Date  Topic  Readings  Notes 
Introduction / background  

Sept. 4  Lecture: Introduction  notes  
Sept. 10  Probability/linear algebra review 

notes 
Sept. 11  Python review 

notes
logsumexp.py 
Learning about images  
Other readings


Sept. 17  Filtering, convolution, phasor signals 
Reading 1: Possible Principles Underlying the Transformations of Sensory Messages. (Barlow, 1961) [pdf] 
notes 
Sept. 18  Fourier transforms on images  notes  
Sept. 24  Sampling, leakage, windowing  Reading 2: sparse coding (Foldiak, Endres; scholarpedia 2008) 
Reading 1 due notes 
Sept. 25  PCA, stationarity and Fourier bases 
notes Assignment1 (worth 10%) 

Oct. 1  PCA and whitening cont'd.  Reading 3: Emergence of SimpleCell Receptive Field Properties by Learning a Sparse Code for Natural Images. (Olshausen, Field 1996). [pdf] 
Reading 2 due 
Oct. 2  Some facts about vision in the brain 
Assignment 1 due solution to question 3 solution to question 4 logreg.py minimize.py (required if using Logreg.train_cg) notes 

Oct. 8  Vision in the brain cont'd. 
Reading 4: The ``independent components'' of natural scenes are edge filters. (Bell, Sejnowski 1997) [pdf] 
Reading 3 due 
Oct. 9  Whitening vs independence, ICA 
Assignment2 (worth 10%) notes 

Oct. 15  ICA cont'd. 
Reading 5: Review backprop (aka error backpropagation).
For example, by watching week 5
of this course
or by reading Bishop, chapter 5. If/once you know backprop, check out theano and these tutorials. 
Reading 4 due 
Oct. 16  Overcomplete codes, energy based models 
Assignment 2 due notes solution to questions 3 and 4 

Oct. 29  Restricted Boltzmann machines  Reading 6: Feature Discovery by Competitive Learning (Rumelhart, Zipser; 1985) [pdf] (email instructor if you cannot access the pdf) 
notes Reading 5 due 
Oct. 30  Competitive Hebbian learning, kmeans 
notes 

Nov. 5  Autoencoders, Convolutional networks 
Reading 7: Pick one of the papers we will discuss on Nov 6 and Nov 12 (other than the one you present) and read it very carefully. In your email, say which one you read. 
notes Reading 6 due 
Nov. 6  Paper presentations, discussion 

Assignment3 (worth 10%) 
Nov. 12  Paper presentations, discussion 
Reading 8: Spatiotemporal energy models for the perception of motion. Adelson and Bergen, 1985 [pdf] 
Reading 7 due 
Learning about motion, geometry, invariance, shape  
Other readings


Nov. 13  Learning about relations 
Assignment 3 due solution to question 2 slides from my cifar tutorial 

Nov. 19  Paper presentations, discussion 
Reading 9: Pick one of the papers we will discuss on Nov 19 and Nov 20 (other than the one you present) and read it very carefully. In your email, say which one you read. DeViSE: A Deep VisualSemantic Embedding Model Frome et al. NIPS 2013 [pdf] (Sebastien O.) Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis. Le, et al. CVPR 2011 [pdf] (David K.) 
Reading 8 due 
Nov. 20  Paper presentations, discussion 
Deep Learning of Invariant Features via Simulated Fixations in Video. Zou et al. NIPS 2012. [pdf] (Gabriel F.) Learning to combine foveal glimpses with a thirdorder Boltzmann machine. H. Larochelle and G. Hinton. NIPS 2010. [pdf] (Eugene V.) 

Nov. 26  Learning about relations cont'd. 
Reading 10: Pick a highly relevant related work for your class project, and read it very carefully. In your email, say which one you read. (due Dec. 3) 
Reading 9 due 
Nov. 27  Project discussions  
Nov. 28, 12:30pm Location: Pav. AndreAisendstadt, rm 3195 
Structured prediction, misc, wrapup 
notes 

