ECE-5424G / CS-5824: Advanced Machine Learning Spring 2019

Course Information

Lectures

• Where: Room 118C, Surge Space Building
  

• When: Monday and Wednesday 2:30 – 3:45 PM
  

Instructor & Teaching Assistants

Jia-Bin Huang	Chen Gao	Shih-Yang Su
Instructor	Teaching Assistant	Teaching Assistant

Prerequisites

• Calculus, Linear Algebra: Check out the Linear algebra review.

• Basic Probability and Statistics: Check out the Probability review

• Proficiency in Python: We will use NumPy and PyTorch for class assignments. Check out the Python review.

Textbook and optional references

Lectures are not based on any particular textbook. Useful references include

• Pattern Recognition and Machine Learning by Christopher Bishop [Link]

• Machine Learning: A Probabilistic Perspective by Kevin Murphy [Link]

• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville [Link]

• Probabilistic Graphical Models by Daphne Koller and Nir Friedman. [Link]

Communication

Please use Piazza for all communications. Please no emails to the instructors or TA.

Disability-related academic adjustments

To obtain disability-related academic adjustments and/or auxiliary aids, students with disabilities must contact the course instructor and the Services for Students with Disabilities (SSD) as soon as possible. To contact SSD you may visit Suite 310 at Lavery Hall, or contact SSD via email ssd@vt.edu or here.

Attendance

Regular attendance is expected. I will post lecture slides on the course website. However, the slides will be difficult to interpret without attending lectures.

Office hour

Academic integrity

Feel free to discuss homeworks with your classmates, but please refrain from showing or sharing any code. Any existing code from the Internet cannot be used in your project assignments unless it is specifically approved by the course instructor. Be sure to acknowledge any help that you do get from other students or outside works, even if its just a small suggestion. Note that violations of academic integrity will go on record at the university, and zero points for the entire project assignment. Please read the following Honor Code pledge.

Coursework

Assignments and Grading

• Homeworks (50%): There are in total seven homework assignments (HW0 - HW6)

• Midterm exam (10 %)

• Final exam (15 %)

• Final project (25% of final grade):

Due dates

All problem sets/reports are to be submitted through Canvas by the due date noted on the assignment. Deadlines are firm.

Late policy

You are expected to do assignments on time. Late assignments will be assigned a penalty of 10% per day. Throughout the term you have an allowance of SIX free late days for your submissions, meaning you can accrue up to five days in late submissions with no penalty.

Final project

The final project is a chance to further explore a topic of interest. Groups of up to four are highly encouraged. More is expected of larger groups. Projects will include a project report webpage. Various types of projects are possible. You could implement a paper that you find interesting, something discussed in class, a significant extension of one of the course projects, or something entirely of your own design. The work does not have to be of publishable originality. However, you are encouraged to submit the revised versions of projects to top machine learning conferences.

• Research project: Perform a project in a topic of your choice. Formulate a goal, devise an approach, and evaluate. When proposing, indicate what dataset you will use for evaluation. For example, you could base your project on an existing paper and try to improve the accuracy or speed with some modification. You could also apply existing algorithms to your own field (e.g., robotics).

• Review and implement a paper: Choose a paper or set of papers and write a scholarly review. Then, implement and evaluate the algorithm. If done in a group, more than one paper should be implemented and compared. Reviews should be written independently for each person, but the group can collaborate on implementation and evaluation.

Credits and Course Notes

The course material builds upon many preceding efforts to design excellent course projects and wonderful course notes. Feel free to use and modify any of the slides for academic and research purposes. Please do credit the original sources where appropriate.