Advanced Machine Learning & Perception

**Internal** Home Handouts News Staff Papers Tutorials **External** ML People
			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: Bilinear and Piecewise Linear Mixtures Bilinear Style and Content by Tenenbaum & Freeman Transformation Invariant Clustering Using EM by Frey & Jojic Topic 3: Dynamical Linear Models and Kalman Filtering Chapter 14 and 17 of Jordan Learning Linear Dynamical Systems by Ghahramani & Hinton 3D Structure from 2D Motion by Jebara, Azarbayejani & Pentland Topic 4: 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 5: Variational Methods Variational Approximation Methods by Jaakkola Dynamical Systems Trees by Howard & Jebara Topic 6: SVMs and Ellipsoidal Machines A Tutorial on SVMs by Burges Ellipsoidal Kernel Machines and Addendum by Shivaswamy & Jebara Tutorial on SVM Regression (Optional) by Smola & Scholkopf Topic 7: Kernels and Unusual Spaces Hilbert Space Methods by R. Kondor Probability Product Kernels by T. Jebara, R. Kondor & A. Howard Fisher Kernels by T. Jaakkola & D. Haussler. Topic 8: 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 9: 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 10: 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 Topic 11: Unlabeled Data and Transduction Text Classification from Labeled and Unlabeled Documents using EM by K. Nigam, A. McCallum, S. Thrun and T. Mitchell Transductive Inference using SVMs by T. Joachims Topic 12: Graphs, Spectral Clustering, Matching Spectral Clustering by U. Von Luxburg B-Matching for Spectral Clustering by Jebara and Shchogolev