Machine Learning Training

Machine Learning Course Content

    Assessing and Comparing Classification Algorithms

  • Cross Validation
  • The Many Faces of ROC Analysis in Machine Learning
  • Classification

  • Decision trees Andrew Moore
  • Tutorial on Practical Prediction Theory for Classification
  • Tutorial on Fusion of Multiple Pattern Classifiers
  • Clustering

  • Spectral Clustering
  • Data Mining

  • A Data Mining tutorial
  • Data mining tutorial
  • Dimensionality reduction

  • Principal Component Analysis and Matrix Factorizations for Learning
  • Spectral Methods for Dimensionality Reduction (part 1) (part 2).
  • Ensemble learning methods

  • A tutorial on Boosting
  • Boosting tutorial
  • Evolutionary Computation

  • A Genetic Algorithm Tutorial
  • Generative methods

  • Graphical models and variational methods: Message-passing and relaxations
  • A Brief Introduction to Graphical Models and Bayesian Networks
  • Graphical models
  • Nonparametric Bayesian Methods
  • Bayesian Methods for Machine Learning
  • Graphical models, exponential families, and variational inference
  • Bayesian Methods for Machine Learning
  • Hidden Markov models

  • A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
  • Markov Random Fields and Stochastic Image Models
  • An Introduction to the Kalman Filter
  • An Introduction to Conditional Random Fields for Relational Learning

    Learning theory

  • Statistical Learning Theory
  • Complexity Theory and the No Free Lunch Theorem
  • A Tutorial on Computational Learning Theory
  • A Geometric Approach to Statistical Learning Theory
  • VC-dimension for characterizing classifiers
  • Neural networks

  • Independent Component Analysis
  • Neural Networks
  • Introduction to Radial Basis Function Networks
  • Parameter estimation/Optimization techniques

  • Optimization for Kernel Methods
  • Expectation-Maximization as lower bound maximization
  • Markov Chain Monte Carlo for Computer Vision
  • Tutorial on variational approximation methods
  • Energy Based Models: Structured Learning Beyond Likelihoods
  • Regression

  • Advances in Gaussian Processes
  • Reinforcement Learning / Q-learning

  • Reinforcement Learning
  • Learning Representation And Behavior: Manifold and Spectral Methods for Markov Decision Processes and Reinforcement Learning
  • Towards Bayesian Reinforcement Learning
  • Significant applications

  • Grammar Induction: Techniques and Theory Colin
  • Bayesian Models of Human Learning and Inference
  • Text mining and internet content filtering
  • Information Extraction, Theory and Practice
  • Probabilistic mechanisms in human sensorimotor control

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