I keep finding cool resources all over the internet, friends send me stuff, and so on and so forth. I always loose track of them so I’m making a little bookmark page to help me keep track of stuff. If you’re reading this and you have something to add or find a dead link please put it in the comments.

Machine Learning 101:

I. Introduction to Machine Learning (cal tech class)

II.  Linear Regression

III) Linear Algebra
online text
– see for usage rights

V) Linear Regression with Multiple Variables
– Gradient Descent (discusses above wiki article)
– Optimization

IV) Octave Tutorial

VI) Logistic Regression (LR) (refers to LR as a classifier)

VII) Regularization
overview using advanced math

VIII and IX) Neural Networks
– backpropagation

XI) Machine Learning System Design

Precision, recall, accuracy, ……

XII) Support Vector Machines

XIII) Clustering

XIV) Dimensionality Reduction

XV) Anomaly Detection

– Google Analytics
– anomaly detection with Google Analytics (example)

Must purchase this article (I did not purchase but appears to be good)

– Gaussian distribution (no math)

XVI) Recommender Systems (using Python)

– Collaborative Filtering

XVII) Large Scale Machine Learning (introduction to class)

– stochastic gradient descent (visualization)……

– parallelized stochastic gradient descent

– recursive partitioning:

Machine Learning 201:

Advanced Machine Learning Course (CMU)

Lecture 1: Machine Learning With Scikit-Learn

Lecture 2: Machine Learning With Scikit-Learn

Lecture 3: Machine Learning from the Boston Python User Group

Andrew Ng’s Standford ML Class

An Introduction to Machine Learning

Andrew Ng’s Coursera Class Wiki

The Machine Learning Library


CMU Google Slides 

NN Course

Some good articles on working with the command line:

command line nuggets for data science (article focuses on unix but all will work in linux bash)

intro to the command line

7 Command Line Tools for Data Scientists

Jacobian Iteration for Singular Value Decomposition:

Basic Explanation 

Stream Algorithm for SVD


Fortran for Beginners 

Fortran 77 Stanford Tutorial

Professional Programmer’s Guide to Fortran 77


Fortran 77 Intrinsic Functions

Mathematics, Statistical Theory and Probability Theory:

Introduction to Probability


Chang Stochastic Processes

Durrett Probability

Very nice list of distributions

Methods of Optimization:

Gradient Descent

Basic Steepest Decent 

Newton’s Method in Optimization

CRAN Optimization and Mathematical Programming Task View

MIT OCW Optimization Methods

Boyd Optimization

Boyd Solutions Manual

Convex Optimization in R

Theoretical Computer Science:

Foundations of Computer Science

Complexity Theory a Modern Approach 

Some Really Random Stuff:

A Little Stats Cheat Sheet. Pretty basic stuff but it is a nice quick reference.

Regex Cheat Sheet

Proof wiki list of symbols with LaTex code!!

LaTex greeks, very useful.

LaTeX fonts


R One pagers

R Time Series

R Statistical And Machine Learning Task View

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