Fall-2020-ITCS-8156-MachineLearning

This repository contains the projects and assignments of course "ITCS 8156: Machine Learning". This course has been taken in Fall 2020 semester, as part of my PhD degree at UNC Charlotte.

Table of contents

Programming Assignments

• Assignment #0 - Data and Visualization
• Assignment #1 - Linear Regression
• Assignment #2 - Classification
• Assignment #3 - Neural Networks
• Assignment #4 - Reinforcement Learning
• Assignment #5 - Deep Learning

Reading Assignments

• Assignment #1 - Linear Model
• Assignment #2 - Linear Classification
• Assignment #3 - Classification
• Assignment #4 - Clustering
• Assignment #5 - Machine Learning Methodology
• Assignment #6 - Neural Networks
• Assignment #7 - Nonlinear Logistic Regression
• Assignment #8 - Reinforcement Learning
• Assignment #9 - Reinforcement Learning with Neural Network Function Approximation

Project

• Understand the Impact of Different Regression Techniques on Updatable Learned Index

Reference

Book Reference

1. Aurelien Geron. Hands-On Machine Learning with Scikit-Learn & TensorFlow. 2nd Ed. O'Reilly, 2019. GitHub
2. Ethem Alpaydin, Introduction to Machine Learning, 3rd edition, MIT Press, 2014.
3. Sutton and Barto, Reinforcement Learning: An Introduction, 2nd Edition (2016), On-line and free.
4. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2015.
5. Kevin P. Murphy, MachineLearning: A Probabilistic Perspective. MIT Press, 2012.
6. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics For Machine Learning, On-line and free. GitHub, Book Site

Awesome Github Repositories

1. handson-ml2: Contains example code and solutions of book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow

Other

1. Online Statistics Education: An Interactive Multimedia Course of Study: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics.