Advanced Machine Learning & Perception

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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: SVMs and Large (Relative) Margin A Tutorial on SVMs by Burges Ellipsoidal Kernel Machines by Shivaswamy & Jebara Relative Margin Margins by Shivaswamy & Jebara Topic 4: Feature and Kernel Selection Feature Selection for SVMs by J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. Vapnik Learning the Kernel for SVMs by G. Lanckriet et al. Topic 5: Maximum Entropy and SVMs Maximum Entropy for Ecology S. Phillips, R. Anderson & R. Schapire Maximum Entropy Discrimination and Multi-Task SVMs by T. Jebara Topic 6: Multi-Task Learning Maximum Entropy Discrimination and Multi-Task SVMs by T. Jebara Topic 7: Kernels and Probabilistic Kernels Exploiting generative models in discriminative classifiers by T. Jaakkola and D. Haussler Probability Product Kernels by T. Jebara, R. Kondor and A. Howard Topic 8: Structured Prediction Cutting-Plane Training of Structural SVMs by T. Joachims, T. Finley and C-N. Yu