Machine Learning and Pattern Recognition: Schedule

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.

Introduction and basic concepts

Subjects treated: Intro, types of learning, nearest neighbor, how biology does it, linear classifier, perceptron learning procedure, linear regression, logistic regression.

Slides: [DjVu | PDF]

Slides Linear Classifiers: [DjVu | PDF]

Recommended Reading:

• Bishop, Chapter 1.
• Refresher on random variables and probabilites by Andrew Moore: (slides 1-27) [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 self-organizing systems and cybernetics, have a look at this book available from the Internet Archive's Million Book Project: Self-Organizing Systems, proceedings of a 1959 conference edited by Yovits and Cameron (DjVu viewer required for full text).

Linear Classifiers, Basis Functions, Kernel Trick, Regularization, Generalization

Subjects treated: Linear machines: perceptron, logistic regression. Linearly parameterized classifiers: Polynomial classifiers, basis function expansion, RBFs, Kernel-based expansion.

Slides Basis Functions, Kernel Trick: [DjVu | PDF]

Slides Regularization, Generalization: [DjVu | PDF]

Recommended Reading:

• Bishop, Chapter 2 and 4.

Kernel Methods and Support Vector Machines

• Slides Support Vector Machines (from Leon Bottou's Spring 2010 course at Princeton): [ DjVu | PDF ]

Other set of slides (with more mathematical details on convex duality and such):

• Slides Support Vector Machines, part 1: [DjVu | PDF]
• Slides Support Vector Machines, part 2: [DjVu | PDF]

Another set of older slides on SVM with more math, if you find that useful:

• Slides Support Vector Machines, part 1: [DjVu | PDF]
• Slides Support Vector Machines, part 2: [DjVu | PDF]

Gradient-Based Learning I, Multi-Module Architectures and Back-Propagation, Regularization

Subjects treated: Multi-Module learning machines. Vector modules and switches. Multilayer neural nets. Backpropagation Learning.

Slides Multi-Module Learning machines, backprop: [DjVu | PDF]

Required Reading: Document Recognition by LeCun, Bottou, Bengio, and Haffner; Part 1 (pages 1 to 5) the first column of page 18: [ DjVu | .pdf ]

Recommended Reading: Bishop, Chapter 5.

Gradient-Based Learning II: Special Modules and Architectures

Slides Special modules: [DjVu | PDF]

Convolutional Nets, Image Recognition

Required Reading: Document Recognition by LeCun, Bottou, Bengio, and Haffner; Part 2 and 3 (pages 5 to page 18) [ DjVu | .pdf ]

Optional Reading: Fu-Jie Huang, Yann LeCun, Leon Bottou: "Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting.", Proc. CVPR 2004. [DJVU, PDF, .PS.GZ,

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, Density Estimation, Maximum Likelihood, Gaussian Estimation, Parzen Windows, PCA, K-Means: [DjVu | PDF]

More on unsupervised Learning, Latent Variables, EM

Slides Latent variables: [DjVu | PDF]

Slides EM, Mixture of Gaussians: [DjVu | PDF]

Intro to Graphical Models

Guest lecture by David Sontag

Slides Intro to Graphical Models, HMM, Belief Propagation: [DjVu | PDF]

Suggested Reading:

• David Mackay's book: Information Theory, Inference, and Learning Algorithms. (available for free download from Mackay's website in PDF and DjVu) to learn about belief propagation in factor graphs.
• Read HMM and graphical model chapters in Bishop.

Energy-Based Models, Loss Functions, Linear Machines

Subjects treated: Energy-based learning, minimum-energy inference, loss functions.

Slides: [DjVu | PDF]

Required Reading:

• Read "A Tutorial on Energy-Based Learning" by LeCun, Chopra, Hadsell, Ranzato, and Huang. [ DjVu | .pdf ]

Structured Prediction: HMMs, CRF, Graph Transformer Networks

with introduction to factor graphs.

Slides:

• Structure Prediction: [DjVu | PDF]

Required Reading:

• Read parts of Gradient-based Learning Applied to Document Recognition by LeCun, Bottou, Bengio, and Haffner; pages 18 (part IV) to the end. [ DjVu | .pdf ]

Optimization for Machine Learning

Required Reading: Efficient Backprop, by LeCun, Bottou, Orr, and Muller: [ DjVu | .pdf ]

Learning Theory, Bagging, Boosting, VC-Dim

Slides Ensemble Methods: [DjVu | PDF]

Sparse Coding and Deep Learning

Slides Sparse Coding, Deep Learning: [DjVu | PDF]