Following are various Machine Learning information sources to review:

- Comparison of algorithms for choosing based on a given dataset

Tammi donated these links:

- Hastie and Tibshirani’s Free Stanford Statistical learning course
- These are the text books (all free of charge and classics)

## Algorithms

Following are additional notes on the various algorithms.

### Support Vector Machines

These are Eric Johnson’s notes from his machine learning course on SVMs:http://focusnumeric.net/PDFs/Stanford%20ML%20-%20Week%207%20Lecture%20Notes.pdf

### Logistic Regression

Note that both Naive Bayesian and Logistic Regression compute p(label|feature), but Naive Bayesian computes it via p(feature|label) and Logistic Regression computes it directly.

Following are some useful links on the Logistic Regression algorithm:

http://www.cs.cmu.edu/~guestrin/Class/10701/slides/logisticregression-decisiontrees.pdfhttp://classes.engr.oregonstate.edu/eecs/spring2012/cs534/notes/Logistic-Regression-5.pdf

https://alliance.seas.upenn.edu/~cis520/dynamic/2014/wiki/index.php?n=Recitations.Classification