machine-learning

Collections of machine learning materials, classical algorithm illustrations and example demonstrations

Reference

Textbooks:

• Machine Learning, by Tom Mitchell.
• Pattern Recognition and Machine Learning, by Christopher Bishop.
• Machine Learning, by Zhihua Zhou.
• An Introduction to Statistical Learning, by Gareth James,Daniela Witten,Trevor Hastie and Robert Tibshirani.

Classes:

• Roni Rosenfeld's Machine Learning Course
• Tom Mitchell's Machine Learning Course
• Andrew Ng's Machine Learning Course

Table of Contents

• Introduction to Machine Learning (Concepts, Methdology
• Model Selection and Evaluation
• Concept learning, inductive bias
• Review of Prob & Stats. Linear Regression
• Information Theory
• Decision trees, overfitting, Occam's razor
• Ensemble Learning (Bagging, Boosting and Random Forest)
• Neural Networks; Deep Learning

List of Implemented Algorithms

• Decision Trees

Otehr Resources

Talks/Tutorials:

Other Projects:

Selected Papers: