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.