# cs229

**Repository Path**: zgzaacm/cs229

## Basic Information

- **Project Name**: cs229
- **Description**: Solutions to the problem sets of CS229: Machine Learning from 2018
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-27
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CS229: Machine Learning

This repository contains my solutions to the problem sets of the 2018 Stanford course CS229 by Andrew Ng.

I organized the solutions in IPython notebooks that can be read online in github. For better readability you can also view the notebooks in [nbviewer](https://nbviewer.jupyter.org/github/Joker14641/cs229/tree/master/).

## Course material

- [Syllabus](http://cs229.stanford.edu/syllabus-autumn2018.html) of the 2018 version of the course
- [Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) of the lectures on YouTube
- [Repository](https://github.com/SKKSaikia/CS229_ML) with the problem sets and many more resources for the course

## Problem sets and solutions

- [Problem set 0: Linear Algebra and Multivariable Calculus](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%200/ps0.pdf)
  - [Solutions](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%200/solutions0.ipynb) Please note that I added a small aside on differentials and Jacobians, which I will consistently use throughout the problem sets though they haven't been covered in the lectures.
  
- [Problem set 1: Supervised Learning](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/ps1.pdf)
  - [1. Linear Classifiers (logistic regression and GDA)](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/PS1E1%20Linear%20Classifiers%20(logistic%20regression%20and%20GDA).ipynb)
  - [2. Incomplete, Positive-Only Labels](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/PS1E2%20Incomplete%2C%20Positive-Only%20Labels.ipynb)
  - [3. Poisson Regression](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/PS1E3%20Poisson%20Regression.ipynb)
  - [4. Convexity of Generalized Linear Models](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/PS1E4%20Convexity%20of%20Generalized%20Linear%20Models.ipynb)
  - [5. Locally weighted linear regression](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%201/PS1E5%20Locally%20weighted%20linear%20regression.ipynb)
  
- [Problem set 2: Supervised Learning II](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/ps2.pdf)
  - [1. Logistic Regression: Training stability](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E1%20Logistic%20Regression%20Training%20stability.ipynb)
  - [2. Model Calibration](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E2%20Model%20Calibration.ipynb)
  - [3. Bayesian Interpretation of Regularization](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E3%20Bayesian%20Interpretation%20of%20Regularization.ipynb)
  - [4. Constructing kernels](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E4%20Constructing%20kernels.ipynb)
  - [5. Kernelizing the Perceptron](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E5%20Kernelizing%20the%20Perceptron.ipynb)
  - [6. Spam classification](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%202/PS2E6%20Spam%20classification.ipynb)
  
- [Problem set 3: Deep Learning & Unsupervised learning](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/ps3.pdf)
  - [1. A Simple Neural Network](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/PS3E1%20A%20Simple%20Neural%20Network.ipynb)
  - [2. KL Divergence and Maximum Likelihood](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/PS3E2%20KL%20divergence%20and%20Maximum%20Likelihood.ipynb)
  - [3. KL Divergence, Fisher Information, and the Natural Gradient](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/PS3E3%20KL%20Divergence%2C%20Fisher%20Information%2C%20and%20the%20Natural%20Gradient.ipynb)
  - [4. Semi-supervised EM](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/PS3E4%20Semi-supervised%20EM.ipynb)
  - [5. K-means for compression](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%203/PS3E5%20K-means%20for%20compression.ipynb)
  
- [Problem set 4: EM, DL, & RL](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/ps4.pdf)
  - [1. Neural Networks: MNIST image classification](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/PS4E1%20Neural%20Networks%20MNIST%20image%20classification.ipynb)
  - [2. Off Policy Evaluation And Causal Inference](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/PS4E2%20Off%20Policy%20Evaluation%20And%20Causal%20Inference.ipynb)
  - [3. PCA](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/PS4E3%20PCA.ipynb)
  - [4. Independent Components Analysis](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/PS4E4%20Independent%20components%20analysis.ipynb) You can find the mixed and unmixed sound files [here](https://github.com/Joker14641/cs229/tree/master/Problem%20Sets/Problem%20Set%204/output)
  - [5. Markov Decision Processes](https://github.com/Joker14641/cs229/blob/master/Problem%20Sets/Problem%20Set%204/PS4E5%20Markov%20decision%20processes.ipynb)