CS 4641 B: Machine Learning (Summer 2020)

Course Overview

This course mainly introduces fundamental techniques in machine learning that widely used in data analysis. Our emphasis is on two parts: the underlying math as algorithms and their applications.

Prerequisite for this course include: 1) basic knowledge of probability, statistics, and linear algebra; 2) Basic programming experiences in python

Office hour:

Piazza will be the main place for any discussions or questions. Students are encouraged to discuss anything on this course, such as unclear parts on the lectures, assignments or corrections on the content. Note that one part of the grading attendance is based on the discussions in piazza. If there is something you do not want to talk in public, Piazza supports private message.

Schedule (Coming soon)

Date Topic Assignment Due Readings
May 11, 2020 Course Overview;
Class Video Lecture; Class Notes;
 GT Honor Code
May 13 Linear Algebra;
Class Video Lecture; Class Notes;
Probability;
Class Video Lecture; Class Notes;
 Linear Algebra Review by Zico Kolter;
SVD tutorial ;
Probability Theory Review by Andrew Moore
May 18 Information theory Information theory;
Class Video Lecture; Class Notes;
 The Differences Between Data, Information and Knowledge;
Entropy vs. Variance
May 20 Linear regression;
Class Video Lecture;
Class Notes;
 Homework 1;
Attendance sheet		 June 3;May 22 Simple linear regression in Matrix format
May 25 No class (Memorial day)
May 27 Regulization;
Class Video Lecture;
Class Notes;
 Regularization integration;
Regularization math
Jun 1 Logistic regression;
Class Video Lecture;
Class Notes;
 Logistic regression vs Linear regression
Jun 3 Project requirement;
Class Video Lecture;
 Homework 2;
Project proposal		 Jun 17th; Jun 14th
Jun 8 Decision Tree;
Class Video Lecture;
Class Notes;
 Intro to Decision Tree
Jun 10 K-means clustering;
Class Video Lecture;
Class Notes;
 Curse of dimensionality;
Kmeans application and analysis
Jun 15 Hierarchical clustering;
Density clustering;
HW2-officehour;
Class Video Lecture;
Class Notes;
 Concept of hierarchical clustering;
Density based clustering
Jun 17 Gaussian Mixture Model;
Class Video Lecture;
Class Notes;
 Homework 3 July 1st Tools and examples of GMM;
GMM and EM algorithm
Jun 22 Principle Component Analysis;
Class Video Lecture;
Class Notes;
Jun 24 SVM;
Jun 29 SVM and Kernalized SVM
July 1 Reinforcement learning
July 6 Gradient decent and neural network
July 8 Deep learning, DRL
July 13 Deep reinforcement learning and Neural network
July 20 Presentation of projects Presentation, Project report July 20th, July 29th

Grading

Project resources and dataset, thanks to Mahdi and Polo