deep-learning deep-learning-algorithms deep-neural-networks deep-reinforcement-learning linear-regression logistic-regression machine-learning machine-learning-algorithms python reinforcement-learning support-vector-machines svm tensorflow

CS446 Machine Learning

Course Information:

The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas:

Discriminative models
Generative models
Reinforcement learning models

In particular we will cover the following:

Linear Regression
Logistic Regression
Support Vector Machines
Deep Nets
Structured Methods
Learning Theory
kMeans
Gaussian Mixtures
Expectation Maximization
Markov Decision Processes
Q-Learning

Pre-requisites:

Probability, Linear Algebra, and proficiency in Python.

Instructors:

Alexander Schwing, Website [Link]
Matus Telgarsky, Website [Link]

Assignments

Assignment 1: Introduction + Python — Design by Colin, Review by Yucheng
Assignment 2: Linear Regression — Design by Raymond, Review by Jyoti
Assignment 3: Binary Classification — Design by Youjie, Review by Jyoti
Assignment 4: Support Vector Machine — Design by Raymond, Review by Ishan
Assignment 5: Multiclass Classification — Design by Yucheng, Review by Safa
Assignment 6: Deep Neural Networks — Design by Safa, Review by Yuan-Ting
Assignment 7: Structured Prediction — Design by Colin, Review by Yucheng
Assignment 8: k-Means — Design by Jyoti, Review by Youjie
Assignment 9: Gaussian Mixture Models — Design by Ishan, Review by Colin
Assignment 10: Variational Autoencoder — Design by Yuan-Ting, Review by Raymond
Assignment 11: Generative Adverserial Network — Design by Ishan, Review by Yuan-Ting
Assignment 12: Q-learning — Design by Safa, Review by Youjie

Announcement:

All copyrights reserved © CS446 Instructors & TAs

Raymond Yeh, Website [Link]
Colin Graber
Safa Messaoud
Yuan Ting Hu
Ishan Deshpande
Jyoti Aneja
Youjie Li
Yucheng Chen

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.