
This page contains the schedule, slide from the lectures, lecture notes, reading lists,
assigments, and web links.
I urge you to download the DjVu viewer
and view the DjVu version of the documents below. They display faster,
are higher quality, and have generally smaller file sizes than the PS and PDF.
WARNING: The schedule below is almost certainly going to change.
Click here for the list of assignments and final projects >>>
Introduction and basic concepts 
Subjects treated: Intro, types of learning, nearest neighbor, how biology does it,
linear classifier, perceptron learning procedure, linear regression,
Slides: [DjVu  PDF]
Recommended Reading:
 Bishop, Chapter 1.
 Refresher on random variables and probabilites by
Andrew Moore: (slides 127) [DjVu  PDF]
 Refresher on joint probabilities, Bayes theorem by
Chris Willams: [DjVu  PDF]
 Refresher on statistics and probabilities by
Sam Roweis: [DjVu  PS]
 If you are interested in the early history of selforganizing
systems and cybernetics, have a look at this book available from the
Internet Archive's Million Book Project: SelfOrganizing
Systems, proceedings of a 1959 conference edited by Yovits and
Cameron (DjVu viewer required for full text).
Basis Functions, Kernel Trick, Regularization, Generalization 
Subjects treated: Energybased models, minimumenergy
machines, loss functions. Linear machines: perceptron, logistic
regression. Linearly parameterized classifiers: Polynomial
classifiers, basis function expansion, RBFs, Kernelbased expansion.
Slides Basis Functions, Kernel Trick: [DjVu  PDF]
Slides Regularization, Generalization: [DjVu  PDF]
Kernel Methods and Support Vector Machines 
EnergyBased Models, Loss Functions, Linear Machines 
Subjects treated: Energybased learning, minimumenergy
inference, loss functions.
Linear machines: least square, perceptron, logistic regression.
Slides: [DjVu  PDF]
Recommended Reading:
GradientBased Learning I, MultiModule Architectures and BackPropagation, Regularization 
Subjects treated: MultiModule learning machines. Vector
modules and switches. Multilayer neural nets. Backpropagation
Learning.
Slides MultiModule Learning machines, backprop: [DjVu  PDF]
Slides Special modules: [DjVu  PDF]
Recommended Reading: Bishop, Chapter 5.
GradientBased Learning II: Special Modules and Architectures 
Convolutional Nets, Image Recognition 
Required Reading:
If you haven't read it already: Gradientbased Learning Applied to
Document Recognition by LeCun, Bottou, Bengio, and Haffner; pages 1 to
the first column of page 18:
[ DjVu  .pdf ]
Optional Reading: FuJie Huang, Yann LeCun, Leon Bottou: "Learning Methods for Generic Object
Recognition with Invariance to Pose and Lighting.", Proc. CVPR 2004.
[DJVU,
PDF,
.PS.GZ,
Required Reading:
Efficient Backprop, by LeCun, Bottou, Orr, and Muller:
[ DjVu  .pdf ]
Probabilistic Learning, MLE, MAP, Bayesian Learning 
Slides Review of Probability and Statistics: [DjVu  PDF]
Slides Bayesian Learning: [DjVu  PDF]
Intro to unsupervised Learning 
Slides Unsupervised Learning, PCA, KMeans: [DjVu  PDF]
More on unsupervised Learning, Latent Variables, EM 
Slides Latent variables: [DjVu  PDF]
Slides EM, Mixture of Gaussians: [DjVu  PDF]
Homework Assignements: KMeans and Mixture of Gaussians:
Learning Theory, Bagging, Boosting, VCDim 
Slides Latent variables: [DjVu  PDF]
Intro to Graphical Models 
Suggested Reading:
Structured Outputs: HMMs, Graph Transformer Networks 
Suggested Reading:
Project Showcase and Demos 

