Machine Learning on Coursera

This is part of an independent study in machine learning done in the Spring of 2017 at Olin College of Engineering.

Running

Each sub-directory should have a ex#.m file that matches the sub-directory name. Homework is done in Octave. Each homework assignment instruction is listed as ex#.pdf.

To run homework code, make sure that you have Octave installed. In each subdirectory, you can run ex# in the Octave console, where # is the homework assignment number.

Latter homework were submitted as pull requests. View them here:

https://github.com/hdavidzhu-cabinet/coursera_ml/pulls?q=is%3Apr+is%3Aclosed

The assignments are from Coursera's Machine Learning course.

Proofs

As part of this independent study, I also explored the proofs behind the convexity of linear and logistic regression. That document can be found here:

https://github.com/hdavidzhu-cabinet/coursera_ml/blob/master/david_zhu_ml_independent_study_proofs.pdf

Kaggle competition

Finally, I also worked through a Kaggle competition on sentiment analysis for Rotten Tomato reviews. That work can be found here:

https://github.com/hdavidzhu/DataScience16CYOA