By Rubens Zimbres. Rubens is a Data Scientist, PhD in Business Administration, developing Machine Learning, Deep Learning, NLP and AI models using R, Python and Wolfram Mathematica. Click here to check his Github page.

Extract from the PDF document

This is a 17 page PDF document featuring a collection of short, one-line formulas covering the following topics (and more):

  • Naive Bayes
  • Mixture Models
  • EM Algorithm
  • Maximum Likelihood
  • Bayesian Networks
  • k-NN, weighted NN
  • Logistic Regression
  • Principal Component Analysis
  • Stochastic Gradient
  • Neural Networks
  • Collaborative Filtering
  • Back Propagation
  • Support Vector Machines
  • Ridge and Lasso Regression

It does look like a cheat sheet with everything you need to know in terms of formulas used in standard machine learning algorithms. It would be helpful if someone could add some meat to this document, in particular, adding some context and explanations for each formula. Many of these formulas are nicely illustrated here (scroll to the bottom of the page after clicking on the link.)

Click here to view the PDF document. 

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Comment by Richard Kenyon on February 21, 2017 at 12:48pm

I have been playing with https://en.wikipedia.org/wiki/Tf%E2%80%93idf

I think it qualifies as a machine learning algorithm and can be usefully combined with Bayes methods.

Used iteratively it helps maintain a list of weight for 'tokens' being managed.

Comment by Ho Yin Chan on February 3, 2017 at 6:09pm

It would nice to have  linear discriminant analysis (LDA) after Principal component analysis (PCA), and probably hyperbolic tangent (tanh) and rectified linear unit function (relu) after sigmoid, and perhaps Kullback–Leibler divergence as well ? :)

Comment by Rubens Zimbres on January 30, 2017 at 4:49am

Thanks for sharing, Vincent !

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