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Machine Learning and Pattern Recognition: Schedule
[ Course Homepage | Schedule and Course Material | Mailing List ]
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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. Full-text search is provided for the entire collection of slides and papers. Click here to search You can have a look at the schedule and class material for the version of this course taught during the Spring 2004 semester, but be warned that the new edition is significantly different.
Subjects treated: Intro, types of learning, nearest neighbor, how biology does it, linear classifier, perceptron learning procedure, linear regression, Recommended Reading:
Subjects treated: Energy-based models, minimum-energy machines, loss functions. Linear machines: perceptron, logistic regression. Linearly parameterized classifiers: Polynomial classifiers, basis function expansion, RBFs, Kernel-based expansion.
Subjects treated: Multi-Module learning machines. Vector modules and switches. Multilayer neural nets. Backpropagation Learning. Intro to Model Selection, structural risk minimization, regularization. Slides on Regularization: [DjVu | PDF | PS] Slides on Multi-Module Back-Propagation: [DjVu | PDF | PS] Required Reading: Gradient-based Learning Applied to Document Recognition by LeCun, Bottou, Bengio, and Haffner; pages 1 to the first column of page 18: [DjVu | .ps.gz ]
Subjects treated: Trainers; complex topologies; special modules; Cross-entropy and KL-divergence; RBF-nets, Mixtures of Experts; Parameter space transforms; weight sharing; convolution module; TDNN; Recurrent nets. Homework Assignements 01: implementing the Perceptron Algorithm, MSE Classifier (linear regression), Logistic Regression. Details and datasets below:
Subjects treated: Convolutional Networks; Image recognition, object detection, and other applications; Convergence of gradient-based optimization and acceleration techniques. Slides: talk on object recognition with convolutional nets: DjVu Slides on optimization: [DjVu | PDF | PS] Required Reading: If you haven't read it already: Gradient-based Learning Applied to Document Recognition by LeCun, Bottou, Bengio, and Haffner; pages 1 to the first column of page 18: [ DjVu | .ps.gz ] Optional Reading: Fu-Jie Huang, Yann LeCun, Leon Bottou: "Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting.", Proc. CVPR 2004. .ps.gz
NO LECTURE Required Reading:
Subjects treated: Refresher probability theory; Bayesian Estimation, Maximum Likelihood Estimation, Maximum A Posteriori Estimation, Negative Log-Likelihood Loss Functions. Slides: Refresher on Probability Theory: [DjVu | PDF | PS] Slides: Bayesian Learning: [DjVu | PDF | PS] Required Reading:
Homework 01 due TODAY!
Subjects treated: Unsupervised Learning: Principal Component Analysis. Density Estimation: Parzen Windows, Mixtures of Gaussians, Auto-Encoders. Latent variables. Intro to the Estimation-Maximization algorithm. Slides:
Subjects treated: Modeling distributions over sequences. Learning machines that manipulate graphs. Finite-state transducers. Graph Transformer Networks. Efficient learning: Newton's algorithm, Levenberg-Marquardt. Required Reading: Note: the slides used in class are not provided because the two following papers cover the material.
Homework Assignements: implementing Gradient-Based Learning and back-propagation. You must implement gradient-based learning using the object-oriented, module-based approach as described in class. Various architectures, including a multilayer neural net, must be implemented and tested on two datasets.
Subjects treated: More on optimization methods for lerning: Gauss-Mewton, Levenberg-Marquardt, BFGS, Conjugate Gradient; Expectation-Maximization Algorithm (EM). Introduction to Hidden Markov Models (HMM). Required Reading:
Subjects treated: HMM learning. Ensemble methods, Full Bayesian Learning, Bagging, Boosting. Learning Theory, Bounds, VC-Dimension. Slides: Homework Assignements: Homework 03: K-Means and Mixture of Gaussians estimation with EM.
Subjects treated: Intro to graphical models, Belief Networks and Factor Graphs, Inference, Belief Propagation, Boltzmann Machines. Homework Assignements: Final Project
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