Statistical Learning Theory CS281a

Created by: Colorado Reed
Intended for: student taking CS281a at Berkeley or studying similar material

This content of roadmap follows Prof. Jordan's lectures/textbook.

Conditional Independence and Factorization

Exact Inference

  • The variable elimination algorithm is based on interchanging sums and products in the definitions of marginals or partition functions but can perform many redundant calculations.
  • the sum product algorithm is a belief propagation algorithm based on dynamic programming. It has the advantage over naive variable elimination in that it reuses computations to compute marginals for all nodes in the graph
  • junction trees generalize the the sum product algorithm to arbitrary graphs by grouping variables together into cliques such that the cliques form a tree.

Sampling-based inference

Statistical Concepts

We discussed Bayesian vs frequentist inference; some topics we touched on include:

Linear Regression and the Least Mean Squares algorithm