Learning Algorithms – or blend of computation and statistics

This page includes my sporadic, useful to read and keep around notes.

  1. Expectation Maximization

  2. Generalized Linear Models

  3. Graphical Models

  4. Topic Modelling

  5. Approximate Inference

    1. Variational Approximation

    2. Expectation Propagation

    3. Sampling based learning

  6. Support Vector Machines

  7. Bayesian Non-parametrics

  8. Posterior Regularization

  9. Online Learning

  10. Boosting

  11. Learning half-spaces

  12. Singular Value Decomposition

  13. Learning Theory

    1. Introduction to Learning Theory and PAC

    2. Concentration Inequalities

    3. VC dimensions bounds

    4. Rademacher bounds

    5. Hardness and Impossibility Results

In case you find any issues/typoes within notes, I would appreciate if you drop me a note.