Ideas for Spring 2016

###Topics Semi-ordered list of topics we'd like to cover in the Spring 2016 research course (please be as specific as possible to narrow down the scope):

• Linear Algebra
  • Matrix Inverse
  • Determinants
  • Eigenvectors + EigenValues
  • Singular Value Decomposition
• Probabilities & Statistics
  • Discrete Distributions
  • Multinomial Logit/Probit
  • Chain Rule
  • Bayesian statistics:
    • Posterior/prior probability

Resources:

• Khan Academy: good resource for a solid understanding of basic concepts. They have a course on linear algebra here.
• Jason's Applied NLP course
• Andrew Ng's Machine Learning course at Stanford (not the coursera course)
• Metacademy gives you a roadmap of what you need to learn before understanding a concept, with links to other resources
• Video lectures by top ML researchers from Machine Learning Summer School (Cambridge 2009)