Course Outline

Introduction

Probability Theory, Model Selection, Decision and Information Theory

Probability Distributions

Linear Models for Regression and Classification

Neural Networks

Kernel Methods

Sparse Kernel Machines

Graphical Models

Mixture Models and EM

Approximate Inference

Sampling Methods

Continuous Latent Variables

Sequential Data

Combining Models

Summary and Conclusion

Requirements

  • Understanding of statistics.
  • Familiarity with multivariate calculus and basic linear algebra.
  • Some experience with probabilities.

Audience

  • Data analysts
  • PhD students, researchers and practitioners
 21 Hours

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