Table of Contents

1. Machine Learning
2. Interesting Algorithms
3. MISC

Machine Learning

• GD/SDG

  • Gradient descent revisited
  • Stochastic Gradient Descent Tricks

• Naive Bayes

  • UW NB Tutorial
  • Text Classification and Naive Bayes
  • Beta-Binomial Model

• Support Vector Machine

  • Stanford CS229 SVM Note
  • An Idiot’s guide to Support vector machines (SVMs)
  • Lagrange Multipliers Tutorial in the Context of Support Vector Machines

• Principle Component Analysis

  • An Introduction to Principal Component Analysis
  • PCA Slides from Columbia statistics department

• Latend Dirichlet Allocation

  • Topic Model by David Blei (Vedio)
  • Original Paper

• Hidden Markov Model

  • The EM algorithm for HMMs, Michael Collins
  • HMM with Gaussian emission

• Variational Inference

  • Tutorial By Blei

• Courses

  • Coursera: Machine Learning
  • Stanford CS229

Interesting Algorithms

• Moore Voting

MISC

• Terminal

  • oh-my-zsh

• Scala

  • Run Scala with Sublime Text3 and sbt

• Rails

  • Deploy Rails & AngularJS on Apache