The final exam will be available from Friday, Dec. 11 to Wednesday, Dec. 16. Stay tuned to Canvas for details.
Homework 5 is out. See the instructions here, starter code and data here, and the LaTeX source here. The homework is due 11/14 at 11:59 pm.
The project page is up (but will be forever under construction). Visit it to see the instructions for the final project. Project proposals will be due with Homework 4
Homework 4 is out. See the instructions here, starter code and data here, and the LaTeX source here. The homework is due 10/31 at 11:59 pm.
Homework 3 is out. See the instructions here, starter code and data here, and the LaTeX source here. The homework is due 10/10 at 11:59 pm.
Homework 2 is out. See the instructions here, starter code and data here, and the LaTeX source here. The homework is due 9/26 at 11:59 pm.
Homework 1 is out. See the instructions here, starter code and data here, and the LaTeX source if it's useful for your writeup here. The homework is due 9/10 at 2 pm.
We have moved to a new room. The class is now being held in Durham 261.
Homework 0 is available on http://canvas.vt.edu. You can preview it here if you aren't registered for the class. Homework 0 won't be graded, but you should be able to comfortably answer all its problems if you adequately satisfy the prerequisites for the course. If you struggle with the problems, you are strongly advised to consider not taking this course.
The course is full, but if you are interested and were unable to register, please come to the first day of class to fill out a form. I will give out the URL and password for the form in the first session.
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.
Instructor: Bert Huang, Assistant Professor of Computer Science
Office hours: Monday 2-3 PM and Tuesdays 3:30 PM - 5:00 PM, Torgersen Hall 3160L email@example.com
Subhodip Biswas, CS PhD Student
Office hours: Fridays 3:30-5:30 PM, 219 Kelly Hall firstname.lastname@example.org
Overview of machine learning: learning from data; overfitting, regularization, cross-validation.
Unsupervised and semi-supervised learning: clustering (k-means, Gaussian mixtures); principal components analysis
Learning theory: probably approximately correct (PAC) learning, model complexity, bias and variance
Structured models: Bayesian networks, Markov random fields, hidden Markov models
Other topics: reinforcement learning, machine learning applications (vision, natural language processing, recommendation)
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.
Machine Learning: a Probabilistic Perspective, by Kevin Murphy. http://www.cs.ubc.ca/~murphyk/MLbook/
This book is for sale. To mitigate your costs, it is officially optional for the course. However, it is well regarded within the machine learning community as one of the more comprehensive and up-to-date resources for the latest knowledge on machine learning, so if you are serious about machine learning, I highly recommend you purchase a copy. It will help you in this class and beyond.
45%: Five homework assignments with written and programming components
10%: Midterm exam
20%: Final project
20%: Final exam
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.
Each homework assignment will include written and programing portions.
Both written and programming assignments will be submitted electronically.
I strongly recommend learning LaTeX to help write neat, readable math, but I will not require LaTeX.
Programming assignments will be in MATLAB.
Homework assignments will be listed in the announcements at the top of the course homepage.
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.
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.
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.
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.