Instructor: Prof. Yanjun (Jane) Qi, (yanjun@virginia.edu)
Lecture is on Tuesday and Thursday from 12:30PM - 13:45PM in Rice Hall.
| Tag | Title and Information | URLs (Video + Slide) | Talk Year | Week No. | Date | |
|---|---|---|---|---|---|---|
Introdution to Large-Scale Machine Learning Topics | ||||||
| ✓Basic | Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning | (PDFSlide) | 2010 | W1 | Tu - 0113 | |
| ✓Basic | Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus | (SyllabusURL) | 2012 | |||
| ✓Basic | William Cohen - CMU Machine Learning with Large Datasets 10-605: Syllabus | (SyllabusURL) | 2014 | |||
Topic I: Deep Learning Topics | ||||||
| ✓Deep | DeepLearningSummerSchool12: Yann LeCun (New York University), Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction (Part1 - deepNN supervised) | (Video) + (PDFslide) | 2012 | W1 | Th - 0115 | |
| ✓Deep | DeepLearningSummerSchool12: Yann LeCun (New York University), Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction (Part2 - deepNN unsupervised) | (Video) + (PDFslide) | 2012 | |||
| ✓DeepStructured | DeepLearningSummerSchool12: Yann LeCun (New York University), Deep Learning, Graphical Models, Energy-Based Models, Structured Prediction (Part3 - deepNN graph transformer network) | (Video) + (PDFslide) + (PDF2) | 2012 | |||
| ✓DeepGM | DeepLearningSummerSchool12: Geoffrey Hinton: Introduction to Deep Learning , Deep Belief Nets (Parts 1 / Relevant Paper: A fast learning algorithm for deep belief nets ) | (Video) + (PDFslide) | 2012 | W2 | Th - 0122 | |
| ✓Hardware Parallel | DeepLearningSummerSchool12: Marc'Aurelio Ranzato (Google Inc.), Large Scale Deep Learning | (Video) + (PDFslide) | 2012 | |||
| ✓Deep | MLSS2005: Yann Lecun, Tutorial of Energy-based models | (Video) +(Slide) | 2005 | W3 | Tu - 0127 | |
| ✓Deep | Geoffry Hinton: Learning Energy-Based Models of High-Dimensional Data | (Video) + (PDFslide) | 2012 | |||
| ✓DeepScaling | KDD14: Yoshua Bengio, Scaling Up Deep Learning | (Video) + (PDFslide) | 2014 | |||
| ✓Deep | Yoshua Bengio (University of Montreal): Representation Learning with auto-encoder / decoder variants | (Video) | 2012 | More | ||
| ✓Deep | EML07: Yoshua Bengio (University of Montreal): Speeding Up Stochastic Gradient Descent | (Video) + (PDFslide) | 2007 | |||
| ✓Theory | DeepLearningSummerSchool12: Nando de Freitas (University of British Columbia) An Informal Mathematical Tour of Feature Learning | (Video) + (PDFslide) | 2012 | |||
| ✓Deep | KDD14: Ruslan Salakhutdinov, Deep Learning | (Video) + (PDFslide) | 2014 | |||
Topic II: Kernel Methods Topics | ||||||
| ✓Kernel | Alexander J. Smola, Kernel methods and Support Vector Machines (Part3 ) | (Video) + (PDFslide) | 2008 | W4 | Tu - 0203 | |
| ✓ParamReduct | Sanjiv Kumar (Columbia EECS 6898), Lecture: Kernel Methods (I: Scaling up kernel methods) | (PDFslide) | 2010 | |||
| ✓Hardware Parallel | ICML08: Fast Support Vector Machine Training and Classification on Graphics Processors | (Video) + (PDFslide) | 2008 | W5 | Tu - 0210 | |
| ✓DataStructure | ECML2007: Large Scale Learning with String Kernels, | (Video) + (PDFslide) | 2007 | |||
| ✓Advanced | Francis R. Bach, INRIA: Multiple kernel learning for multiple sources | (Video) + (PDFslide) | 2008 | W6 | Tu - 0217 | |
| ✓Random | Sanjiv Kumar (Columbia EECS 6898), Lecture: Randomized Algorithms | (PDFSlide) | 2010 | |||
| ✓Random | ECML2007: Efficient Machine Learning using Random Projections | (Video) | 2007 | |||
| ✓Random | NIPS2007: Random features for large-scale kernel machines (original paper PDF) + ECCV2012: Fourier Kernel Learning | (Video) + (PDFslide) | 2007 | W7 | Tu - 0224 | |
| ✓Random | Fast Random Feature Expansions for Nonlinear Regression | (Video) + (PDFslide) | 2010 | |||
| ✓FastOptim | NIPS2010: Multiple Kernel Learning and the SMO Algorithm | (Video) + (PDFPaper) | 2010 | |||
| ✓FastOptim | Fast training of support vector machines using sequential minimal optimization. In Book: Advances in Kernel Methods - Support Vector Learning, MIT Press | (PaperPDF) | 1999 | W8 | Tu - 0303 | |
| ✓FastOptim | JMLR 2005: Working Set Selection Using Second Order Information for Training Support Vector Machines | (PaperPDF) | 2005 | |||
| ✓Kernel | NIPS2009: Fast Subtree Kernels on Graphs | (Video) | 2009 | More | ||
| ✓Kernel | NIPS09: Locality-Sensitive Binary Codes from Shift-Invariant Kernels | (Video) + (PDF) | 2009 | |||
| ✓Kernel | PASCAL07: Graph kernels and applications in chemoinformatics | (Video) | 2007 | |||
| ✓Kernel | S.V.N. Vishwanathan, Random walk graph kernels and rational kernels | (Video) | 2007 | |||
| W9: SPRING RECESS | ||||||
Topic III: Optimization for ML or High-Dim/Sparsity Topics | ||||||
| ✓SparsityOptim | KDD08: Trevor Hastie: Regularization Paths and Coordinate Descent | (Video)+ (PDFslide) | 2008 | W10 | Tu - 0317 | |
| ✓Sparsity | ICML09: Group Lasso with Overlaps and Graph Lasso. (Original Paper PDF) |
(Video) | 2009 | |||
| ✓Basic | Mark Schmidt's Note: Least Squares Optimization with L1-Norm Regularization | (NotePDF) | 2005 | |||
| ✓Basic | Mark Schmidt's MLSS2015 tutorial: | (Video) + (Slide) | 2015 | |||
| ✓ Optim Sparse | Mark Schmidt: Fast Non-Smooth and Big-Data Optimization | (Video) | 2014 | |||
| ✓ Optim basic | Convex Optimization and Applications - Stephen Boyd | (Video) | 2015 | |||
| ✓FastOptim | Sanjiv Kumar (Columbia EECS 6898), Lecture: Kernel Methods (II: fast optimization of kernel methods) | (PDFslide) | 2010 | W11 | Tu - 0324 | |
| ✓FastOptim | Sanjiv Kumar (Columbia EECS 6898), Lecture: Large-Scale Optimization Techniques | (PDFslide) | 2010 | |||
| ✓Optim | MLSS2013: Stephen Wright (University of Wisconsin-Madison) Optimization 1-3 | (video) + (slide) | 2013 | |||
| ✓Sparsity | Sanjiv Kumar (Columbia EECS 6898), Lecture: Sparse Methods |
(PDFslide) | 2010 | |||
| Optim | DeepSummer12: Jorge Nocedal (Northwestern University) Tutorial on Optimization methods for machine learning | (video) + (PDFslide) | 2012 | W12 | Tu - 0331 | |
| OptimDiscrete | MLSS2014: Submodularity and Optimization -- Jeff Bilmes | (VideoI-III)+ (PDFslide) | 2014 | |||
| Optim | DeepSummerSchool12: Stephen Wright (University of Wisconsin-Madison) Some Relevant Topics in Optimization (PartI+II) | (video) + (PDFslide) | 2010 | |||
| SparsityOptim | DeepSummer12: Stephen Wright (University of Wisconsin-Madison) Sparse and Regularized Optimization | (video) + (PDFslide) | 2012 | |||
| Sparse | NIPS2009 tutorial: Francis R. Bach: Sparse Methods for Machine Learning: Theory and Algorithms | (video) + (PDFslide) | 2009 | More | ||
| Sparse | MLSS09: Emmanuel Candes, An Overview of Compressed Sensing and Sparse Signal Recovery via L1 Minimization | (Video) | 2009 | |||
| HighDim | NIPS2010: Peter Buhlmann, High-dimensional Statistics: Prediction, Association and Causal Inference | (Video)+ (PDFslide) | 2011 | |||
| HighDim | Martin J. Wainwright, High-Dimensional Statistics: Some progress and challenges ahead | (PDFslide) | 2010 | |||
| HighDim | AISTAT11: Martin J. Wainwright, Convex Relaxation and Estimation of High-Dimensional Matrices | (Video)+ (PDFslide) | 2011 | |||
| OptimAdvance | NIPS10 tutorial: Stephen J. Wright: Optimization Algorithms in Machine Learning Tutorial | (video) + (PDFslide) | 2010 | |||
| OptimDiscrete | NIPS12: Satoru Fujishige, Submodularity and Discrete Convexity | (Video) | 2012 | |||
| OptimDiscrete | ICML13: Tutorial, Submodularity In Machine Learning New-Directions | (Video) | 2013 | |||
| OptimDiscrete | NIPS11: Francis R. Bach, Learning with Submodular Functions: A Convex Optimization Perspective | (Video) | 2011 | |||
| MinMax | ICM2014 VideoSeries IL12.13 : Martin Wainwright on constrained form of statistical MinMax, Privacy, Communication and Computation | (Video) | 2014 | |||
Topic IV: Graphical Model Topics | ||||||
| ✓GM | MLSS2006: Sam Roweis, Machine Learning, Probability and Graphical Models (Part 1-4) | (Video)+ (PDFslide) | 2007 | W13 | Tu - 0407 | |
| ✓GM | MLSS2012: Martin J. Wainwright: Tutorial Materials on Graphical Models, Variational Methods and Message-Passing (PDFNote) | (Video-07)+ (Part1)+ (Part2)+ (Part3) | 2012 | |||
| ✓MCMC Basic | MLSS2009: Iain Murray : Markov Chain Monte Carlo | (Video)+ (Slide) | 2009 | |||
| GM | MLSS2007: Zoubin Ghahramani, Graphical models (Part 1-6) | (Video)+ (PDFslide) | 2007 | W14 | Tu - 0414 | |
| ✓GMTopic | MLSS2009: David Blei, Topic Models (Part I+II) | (Video)+ (PDFslide) | 2009 | |||
| GM | Wainwright and Jordan monograph: More advanced material on exponential families, duality, and variational methods |
(PDFpaper) | 2008 | |||
| GM | MLSS2008: Nando de Freitas, Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making (Part 1-6) | (Video) + (PDFslide) | 2008 | W15 | Tu - 0421 | |
| GMScaling | (LSOLDM)2013: Nando de Freitas, Bayesian Optimization in a Billion Dimensions via Random Embeddings | (Video)+ (PDFslide) | 2013 | |||
| GMScaling | KDD2011: Ron Bekkerman, Misha Bilenko and John Langford, Scaling Up Graphical Model Inference | (PDFSlide) | 2011 | |||
| GM | Gaussian Processes in Practice Workshop 2006, David MacKay, : Gaussian Process Basics | (Video)+ (PDFslide) | 2006 | More | ||
| GMScaling | MLSS2009: Tom Minka, Microsoft Research, Approximate Inference | (Video) + (PDFslide) | 2009 | |||
| GM | MLSS12: Dilan Gorur, Yahoo! Research, Dirichlet Process: Practical Course | (Video)+ (PDFpaper) | 2012 | |||
| GM | DeepSummer12: Iain Murray (University of Edinburgh) Density estimation | (Video)+ (PDFpaper) | 2008 | |||
| GM | ICML13 Tutorial: Gal Elidan, Copulas in Machine Learning (Part I+II) | (Video) | 2013 | |||
| GMScaling | NIPS09: Pedro Domingos, Large-Scale Learning and Inference: What We Have Learned with Markov Logic Networks | (Video)+ (PDFslide) | 2009 | |||
| ✓GMScaling | KDD14: Pedro Domingos, Principles of Very Large Scale Modeling | (Video) + (PDFslide) | 2014 | |||
| GMScaling | Ralf Herbrich, Distributed, Real-Time Bayesian Learning in Online Service | (Video) + (PDFpaper) | 2013 | |||
| W16 (Tu -0428): Project Presentation | ||||||
| EXTRA READINGs from here | ||||||
Topic V: Assorted: structured, low-rank, Metric, and more | ||||||
| ✓Metric | ICML07 Best Paper - Information-Theoretic Metric Learning | (Video) + (PDF) | 2007 | Summer15 | ||
| ✓Structured | CIKM08: Charles Elkan, Log-linear Models and Conditional Random Fields | (Video) + (PDF) | 2008 | |||
| ✓Structured | ECML2012: Thomas Gartner, Fraunhofer IAIS , Algorithms for Predicting Structured Data (Part 1-3) | (Video) + (PDF) | 2012 | |||
| ✓Structured | MLG08: Thorsten Joachims, Structured Output Prediction with Structural SVMs | (Video) + (PDF) | 2008 | |||
| ✓Matrix | MLSS2009 : Emmanuel Candes, Department of Statistics, Stanford University : Tutorial, Matrix Completion via Convex Optimization: Theory and Algorithms | (Video) | 2009 | |||
| ✓LowRank | MLSS2011: Emmanuel Candes, Department of Statistics, Stanford University, Title: Low-rank modeling | (Video) + (PDF) | 2011 | |||
| ✓LowRank | Sanjiv Kumar (Columbia EECS 6898), Lecture: Matrix Approximations (Part I + Part II) | (PDF-1)+ (PDF-2) | 2010 | |||
| ✓LowRank | ICML13 Tutorial: Tensor Decomposition Algorithms for Latent Variable Model Estimation | (Video) | 2013 | |||
| ✓Spectral | Sham Kakade, Scalable Spectral Approaches for Learning Topics, Clusters, and Communities (JMLR paper: Tensor Decompositions for Learning Latent Variable Models) | (Video) + (PDFpaper) | 2014 | |||
| ✓Spectral | Arik Azran, Department of Engineering, University of Cambridge: Tutorial, Spectral Clustering | (Video) + (PDF) | 2008 | |||
| ✓DimReduct | Sanjiv Kumar (Columbia EECS 6898), Lecture: Dimensionality Reduction: | (PDF) | 2010 | |||
| ✓ApproxNN | Sanjiv Kumar (Columbia EECS 6898), Lecture: Approximate Nearest Neighbor Search (Part I + Part II) | (PDF-1)+ (PDF-2) | 2010 | |||
Topic VI: Scalable / Parallel / Random / Streaming Related Topics | ||||||
| Hashing | John Langford, NYU Course on Big Data, Large Scale Machine Learning - Feature Hashing | (Video) | 2012 | Summer16 | ||
| Basic | Alex Smola: Scalable ML Course: statistics | (Video) | 2012 | |||
| System | Alex Smola: Scalable ML Course: System | (Video) | 2012 | |||
| scalable | Alex Smola: MLSS 2014: Scalable machine learning | (Video) | 2014 | |||
| random | Michael Mahoney on Recent Results in Randomized Numerical Linear Algebra (NIPS 2013 Workshop on Randomized Algorithms) | (Video) | 2013 | |||
| random | Francis Bach on Beyond stochastic gradient descent for large-scale machine learning (NIPS 2013 Workshop on Randomized Algorithms) | (Video) | 2013 | |||
| random | Gautam Dasarathy: Sketching Sparse Covariance Matrices (NIPS 2013 Workshop on Randomized Algorithms) | (Video) | 2013 | |||
| Reinforcement | Reinforcement Learning: Michael Littman, MLSS 2009 | (Video) + (Slide) | 2009 | |||
| many more exciting video tutorials @ http://videolectures.net | ||||||