IAPR

Tutorials on topics in machine learning


  1. Assessing and Comparing Classification Algorithms
    1. Cross Validation Andrew Moore
    2. The Many Faces of ROC Analysis in Machine Learning Peter A. Flach, ICML'04
  2. Classification
    1. Decision trees Andrew Moore
    2. Tutorial on Practical Prediction Theory for Classification John Langford, JMLR'05
    3. Tutorial on Fusion of Multiple Pattern Classifiers Fabio Roli AI-IA 2003
  3. Clustering
    1. Spectral Clustering Chris H.Q. Ding, ICML'04
  4. Data Mining
    1. A Data Mining tutorial Graham Williams, Markus Hegland and Stephen Roberts
    2. Data mining tutorial LIS - Rudjer Boskovic Institute
  5. Dimensionality reduction
    1. Principal Component Analysis and Matrix Factorizations for Learning Chris H.Q. Ding, ICML'05
    2. Spectral Methods for Dimensionality Reduction (part 1) (part 2) Lawrence Saul, 2005.
  6. Ensemble learning methods
    1. A tutorial on Boosting Yoav Freund and Robert Schapire
    2. Boosting tutorial Ron Meir
  7. Evolutionary Computation
    1. A Genetic Algorithm Tutorial Darrell Whitley
  8. Generative methods
    1. Graphical models and variational methods: Message-passing and relaxations (Martin Wainwright)
    2. A Brief Introduction to Graphical Models and Bayesian Networks Kevin Murphy, 1998
    3. Graphical models David Heckerman, UAI'99
    4. Nonparametric Bayesian Methods Michael I. Jordan NIPS'05
    5. Bayesian Methods for Machine Learning Zoubin Ghahramani, ICML'04
    6. Graphical models, exponential families, and variational inference (Martin Wainwright, Michael Jordan)
    7. Bayesian Methods for Machine Learning Radford Neal, NIPS'04
  9. Hidden Markov models
    1. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition Lawrence R. Rabiner, Proceedings of the IEEE, 1989
    2. Markov Random Fields and Stochastic Image Models Charles A. Bouman ICIP'95
    3. An Introduction to the Kalman Filter Greg Welch and Gary Bishop
    4. An Introduction to Conditional Random Fields for Relational Learning Charles Sutton and Andrew McCallum
  10. Learning theory
    1. Statistical Learning Theory Olivier Bousquet & Bernhard Schölkopf
    2. Complexity Theory and the No Free Lunch Theorem Darrell Whitley, Jean Paul Watson, 2005
    3. A Tutorial on Computational Learning Theory Vasant Honavar, 1997
    4. A Geometric Approach to Statistical Learning Theory Shahar Mendelson, NIPS'04
    5. VC-dimension for characterizing classifiers Andrew Moore
  11. Neural networks
    1. Independent Component Analysis Aapo Hyvärinen and Erkki Oja, in Neural Networks
    2. Neural Networks Ingrid F. Russell
    3. Introduction to Radial Basis Function Networks Mark J. L. Orr, 1996
  12. Parameter estimation/Optimization techniques
    1. Optimization for Kernel Methods S. Sathiya Keerthi in MLSS, Canberra'06
    2. Expectation-Maximization as lower bound maximization Thomas P. Minka, 1998
    3. Markov Chain Monte Carlo for Computer Vision Song-Chun Zhu, Frank Dellaert and Zhuowen Tu, ICCV 2005
    4. Tutorial on variational approximation methods Tommi S. Jaakkola, NIPS'00
    5. Energy Based Models: Structured Learning Beyond Likelihoods Yann LeCun, NIPS'06
  13. Regression
    1. Advances in Gaussian Processes Carl Edward Rasmussen in NIPS 2006
  14. Reinforcement Learning / Q-learning
    1. Reinforcement Learning Satinder Singh NIPS'05
    2. Learning Representation And Behavior: Manifold and Spectral Methods for Markov Decision Processes and Reinforcement Learning Sridhar Mahadevan and Mauro Maggioni, ICML'06
    3. Towards Bayesian Reinforcement Learning Pascal Poupart, NIPS workshop'06
  15. Significant applications
    1. Grammar Induction: Techniques and Theory Colin de la Higuera and Tim Oates, ICML'06
    2. Bayesian Models of Human Learning and Inference J. Tenenbaum, NIPS'06
    3. Text mining and internet content filtering José María Gómez Hidalgo, ECML/PKDD'02
    4. Information Extraction, Theory and Practice Ronen Feldman, ICML'06
    5. Probabilistic mechanisms in human sensorimotor control Daniel Wolpert, NIPS'04

Online Video Tutorials

  1. Clustring
    1. Lectures on Clustering (Ulrike von Luxburg, 3:24')
    2. Game Theory & Clustering (Marcello Pelillo, 2:56')
    3. Clustering the Tagged Web (Hector Garcia-Molina, et. al., 0:30')
    4. Multi-Assignment Clustering for Boolean Data (Mario Frank, 0:25')
    5. Extracting Semantic Networks from Text via Relational Clustering (Pedro Domingos, Stanley Kok, 00:20')
  2. Ensemble Methods
    1. From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods (Giovanni Seni, John Elder, 2:45')
    2. An Introduction to Ensemble and Boosting (Amir Saffari, 0:16')
    3. Overview of New Developments in Boosting (Joseph K. Bradley, 0:47')
    4. Large-Margin Thresholded Ensembles for Ordinal Regression (Hsuan-Tien Lin, 0:17')
    5. Identifying Feature Relevance using a Random Forest (Jeremy D. Rogers, 0:26')
  3. Markov Processes
    1. A Tutorial Introduction to Stochastic Differential Equations: Continuous-time Gaussian Markov Processes (Chris William, 0:42')
    2. Markov Chain Monte Carlo Methods (Christian Rober, 3:52')
    3. Sequential Monte Carlo methods (Arnaud Doucet, 2:16')
    4. Abstraction Augmented Markov Models (Adrian Silvescu, 0:21')
    5. Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (Ruslan Salakhutdinov, 0:24')
  4. Computational Learning Theory
    1. Online Learning and Game Theory (Adam Kalai, 1:37')
    2. Who is Afraid of Non-Convex Loss Functions? (Yann LeCun, 0:59')
    3. Online Learning and Bregman Divergences (Manfred K. Warmuth, 3:32')
    4. Graph complexity for structure and learning (John Shawe-Taylor, 0:32')
    5. Entropy Properties of a Decision Rule Class in Connection with machine learning abilities (Alexey Chervonenkis, 0:45')
  5. Neural Networks
    1. Neural control - Layers, Loops, Learning (Florentin Worgotter,0:58')
    2. Implications of decoding for theories of neural representation (James Haxby, 0:36')
    3. Exploring Spatially Embedded Artificial Neural Networks (Patricia Vargas, 0:57')
    4. Mixtures of Neural Nets (Edward Snelson, 0:30')
    5. Convolutional Object Finder, A Neural Architecture for Fast and Robust Object Detection (Christophe Garcia, 0:22')
  6. Reinforcement Learning
    1. Introduction to Reinforcement Learning and Bayesian learning (Mohammad Ghavamzadeh, 0:20')
    2. Reinforcement learning (Scott Sanner, 5:09')
    3. Introduction to Reinforcement Learning (Csaba Szepesvar, 5:47')
    4. Model-Based Reinforcement Learning (Michael Littman, 1:54')
    5. Reinforcement Learning (Satinder Singh, 4:58')
  7. Regression
    1. Multiplicative Updates for L1-Regularized Linear and Logistic Regression (Lawrence Saul, 0:24')
    2. Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management (Antonio Salmeron, 0:20')
    3. Large-Margin Thresholded Ensembles for Ordinal Regression (Hsuan-Tien Lin, 0:17')
    4. Utility-Based Regression (Luis Torgo, 0:02')

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