CS5824/CS4824/ECE5424/ECE4424: Machine Learning, Fall 2015



This course will cover the basics of machine learning. It will focus on the science and research behind machine learning. The course will cover mathematical foundations and analysis of machine learning methods and how they work.

Class meets Tuesday and Thursday from 2:00 PM to 3:15 PM in Durham 261.



The listed prerequisite courses cover relevant material that includes: data structures, algorithms, complexity, linear algebra, and basic concepts of probability and statistics (random variables, expectation, conditional distributions, Bayes rule, sampling distributions, estimators, likelihood, and maximum likelihood). The homework assignments will include programming portions using MATLAB.

Please speak with the instructor if you are concerned about your background. Note: If any student needs special accommodations because of any disabilities, please contact the instructor during the first week of classes.

Reading and materials

This class will be taught using a combination of materials. The main book we will use is for sale, but because there are good books freely available online, any assigned reading from that book will also include closely equivalent references to free resources.

Grading breakdown

Format and attendance

The class is designed with the aim to keep the in-class experience interactive, engaging, and fun. As much as possible, I will present dense and dry material in online video lectures that you will watch on your own time, and we will spend class time discussing your questions, details you are interested in, and working together on the homework problems.

To make sure this experience works for everyone, class attendance is mandatory. I will take attendance and penalize unexused absences at my discretion. More importantly, since we will be working on your homework problems in class, missing these discussions will put you at a significant disadvantage.


Course schedule

The course schedule is viewable as a Google spreadsheet at http://bit.ly/5824schedule. The topic schedule is very tentative, but should give you a rough idea of what we're planning on working on.

Video Lectures

Video lectures will be posted on the YouTube playlist https://www.youtube.com/playlist?list=PLUenpfvlyoa0rMoE5nXA8kdctBKE9eSob.

PDF versions of slides from the video lectures are available at http://courses.cs.vt.edu/cs5824/Fall15/pdfs/.


Exams for this course will be open-book and notes. They will be designed with the intent of testing your ability to understand and apply the concepts we learn about in class, not whether you can memorize them. The only restriction to avoid communication with others is that you won’t be allowed to use electronic tools like laptops and smartphones during the exams.

The midterm exam will be held in class. The final exam will be a take-home exam.

Regrading requests

Requests for regrading due to grading errors must be submitted in writing to the TA within one week of the release of grades.

Late homework policy

Homework submitted late without permission will be penalized according to the following formula: (Penalized score) = (Your raw score) * (1 - 0.1*(# of days past deadline))

This formula will apply for up to three days, after which the homework will not be accepted and you will receive a grade of zero. Avoid invoking these penalties by starting early and seeking extra help.

Academic integrity

The tenets of the Virginia Tech Graduate Honor Code will be strictly enforced in this course, and all assignments shall be subject to the stipulations of the Graduate Honor Code. For more information on the Graduate Honor Code, please refer to the GHS Constitution at http://ghs.graduateschool.vt.edu.

This course will have a zero-tolerance philosophy regarding plagiarism or other forms of cheating. Your homework assignments must be your own work, and any external source of code, ideas, or language must be cited to give credit to the original source. I will not hesitate to report incidents of academic dishonesty to the graduate school.

Disclaimer: This syllabus details the plans for the course, which are subject to change. I will make sure any changes are clearly announced and will always be intended for your benefit.

For visitors outside the course: you are welcome to use the course materials for educational purposes. Do not sell any of this content.