Advanced Machine Learning

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			Papers Read the following papers before each class. Topic 1: Gaussians, Linear Models and PCA Gaussian/PCA Face Recognition by Moghaddam, Jebara & Pentland PCA on Natural Images by Rao & Ballard Topic 2: Nonlinear Manifold Learning Locally Linear Embedding by Saul & Roweis Kernel PCA by Scholkopf, Smola & Muller Semidefinite Embedding by Weinberger, Sha & Saul Minimum Volume Embedding by Shaw & Jebara Topic 3: Maximum Entropy, Discrimination and SVMs A Maximum Entropy Approach to Natural Language Processing by Berger, Della Pietra & Della Pietra Multitask Sparsity via Maximum Entropy Discrimination by Jebara (only need to read until pages 75-83 for now) Topic 4: Logistic Regression and Conditional Random Fields Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data by Lafferty, McCallum & Pereira Majorization for CRFs by Jebara & Choromanska Topic 5: Graphical Models and Structured Output Prediction Review Chapters 11, 16 and 17 of Jordan Cutting-Plane Training of Structural SVMs by Joachims, Finley & Yu Structured Prediction with Relative Margin by Shivaswamy & Jebara Topic 6: Beyond Junction Tree: High Tree-Width Models Loopy Belief Propagation for Bipartite Maximum Weight b-Matching by Huang and Jebara Perfect Graphs and Graphical Modeling by Jebara Topic 7: Kernels and Structured Input Spacess Exploiting generative models in discriminative classifiers by Jaakkola and Haussler Probability Product Kernels by Jebara, Kondor and Howard Topic 8: Feature and Kernel Selection Feature Selection for SVMs by Weston, Mukherjee, Chapelle, Pontil, Poggio and Vapnik Learning the Kernel for SVMs by Lanckriet et al. Topic 9: Multi-Task Learning Multitask Sparsity via Maximum Entropy Discrimination by Jebara (read rest of it) Topic 10: Semi-Supervised Learning Transductive Inference using SVMs by Joachims Text Classification from Labeled and Unlabeled Documents using EM by K. Nigam, A. McCallum, S. Thrun and T. Mitchell Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions by Zhu, Ghahramani and Lafferty Local and Global Consistency by Zhou et al. Graph Construction and b-Matching for Semi-Supervised Learning by Jebara, Wang and Chang Topic 11: Clustering, Graphs, Spectra and Matching Spectral Clustering by U. Von Luxburg B-Matching for Spectral Clustering by Jebara and Shchogolev Geometry, Flows, and Graph-Partitioning Algorithms by Arora, Rao and Vazirani Topic 12: Boosting A Short Introduction to Boosting by Y. Freund and R. Schapire Rapid Object Detection using a Boosted Cascade of Simple Features by P. Viola and M. Jones