Machine Learning Resources

I. Introduction to Machine Learning

II.  Linear Regression

III) Linear Algebra

V) Linear Regression with Multiple Variables
– Gradient Descent

– Optimization

IV) Octave Tutorial

VI) Logistic Regression (LR)

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 http://www.google.com/analytics/
– anomaly detection with Google Analytics (example)

Must purchase this article (I did not purchase but appears to be good) http://www.sciencedirect.com/science/article/pii/S138912860700062X

– Gaussian distribution

XVI) Recommender Systems

– Collaborative Filtering

XVII) Large Scale Machine Learning

– stochastic gradient descent

– parallelized stochastic gradient descent

– recursive partitioning:

XVIII) Reinforcement Learning

Machine Learning 201:

Online Lectures:

Deep Learning:

Sparse Coding:

Useful for Kaggle

Some good articles on working with the command line:

Jacobian Iteration for Singular Value Decomposition:

Mathematics, Statistical Theory and Probability Theory:

Methods of Optimization:

Theoretical Computer Science:

Random but Important Things:

R:

Python:

Fortran:

Miscellaneous Links: