Machine Learning, Probability and Graphical Models

Posted in Science on September 07, 2008

Machine Learning, Probability and Graphical Models

Lecture slides:

  • Probabilistic Graphical Models
  • Building Intelligent Computers
  • Uncertainty and Artificial Intelligence (UAI)
  • Applications of Probabilistic Learning
  • Canonical Tasks
  • Representation
  • Using random variables to represent the world
  • Structure of Learning Machines
  • Loss Functions for Tuning Parameters
  • Training vs. Testing
  • Sampling Assumption
  • Generalization and Overfitting
  • Capacity: Complexity of Hypothesis Space
  • Inductive Bias
  • Probabilistic Approach
  • Joint Probabilities
  • Conditional Independence
  • Probabilistic Graphical Models
  • Directed Graphical Models
  • Example DAG
  • Conditional Independence and Missing Edges in DAGs
  • Explaining Away
  • What's Inside the Nodes/Cliques?
  • Probability Tables & CPTs
  • Exponential Family
  • Nodes with Parents
  • Review: Goal of Graphical Models
  • Learning Graphical Models from Data
  • Basic Statistical Problems

Author: Sam Roweis, Department Of Computer Science, University Of Toronto

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Graphical Models