CS229-python-kit

A kit of starter code for CS229 Machine Learning course problem sets

🚨DISCLAIMER

1. All the intellectual property belongs to Stanford University and the faculty members who developed the course. The kit is purely for academic purpose.
2. I was careful not to include any content that would constitute academic dishonesty. My code is either a direct translation of the provided MATLAB starter code or just for reading in file or helping in debugging. But should any problem arises please feel free to contact me.
3. I make NO guarantee that this kit doesn't contain ANY error that would affect the result. If you encounter any error that is indeed due to the implementation feel free to tell me in the Issue section.

HW1

Logistic Regression problem

Data sets

• http://cs229.stanford.edu/ps/ps1/logistic_x.txt
• http://cs229.stanford.edu/ps/ps1/logistic_y.txt

Provided

• utils for reading logistic files.

Regression for denoising quasar spectra

Data sets

• Train set
• Test set

HW2

Spam classification

I could not provide information on this

Provided

• MatrixReader for reading the matrix file
• NaiveBayesSpamDetection and SVMSpamDetection are templates to help you get started. Just implement the Naive Bayes and SVM respectively.

Boosting and high energy physics

Data set and sample code in MATLAB.

I didn't finish this problem. So it would not be very responsible for me to provide my starter code for this one until I redo it and prove it works.

HW3

K-means for compression

The beloved simian images

• http://cs229.stanford.edu/ps/ps3/mandrill-small.tiff
• http://cs229.stanford.edu/ps/ps3/mandrill-large.tiff

Provided

image-compression is a template that reads in the matrix, and output the compressed image.

HW4

Independent Components Analysis

The files are five segments of .wav files available on afs.

Provided

bellsej.py is a skeleton that's basically a line-by-line translation of bellsej.m.

Reinforcement Learning: The inverted pendulum

No data set is required for this task.

Provided

control.py and cartpole_sim.py are direct tranalations of the original simulator and control logic. See the README file under reinforcement/ for more information.