What is deep learning?
Deep learning is a type of machine learning and an approach to artificial intelligence. By building complex representations of the world in terms of simpler ones, deep learning techniques have achieved state-of-the-art results in computer vision, speech recognition, natural language processing, clinical applications, and other areas — promising (and threatening) to transform society.
What is this course?
This graduate course introduces students to the theory and practice of deep learning. By the end of the course, student should understand fundamental concepts and foundational work in the field, know how to choose between and implement different deep learning models to solve substantive problems, and be able to evaluate and critique work that uses deep learning techniques.
What is this webpage?
This webpage provides details for this course, including a schedule. The course syllabus contains course policies and other information. Announcements during lecture provide other updates.
Key links, locations, and times
Course syllabus
Paper presentation schedule and rubric
Project rubric
Assignment submissions: link
Textbook: Deep Learning. Goodfellow, Bengio, and Courville. MIT Press, First Edition
Lecture location: Woodruff Memorial Research Building, 4004
Lecture time: Mondays and Wednesdays, 4:00pm–5:15pm
Instructor
Name: Matthew Reyna
Email address: matthew.a.reyna@emory.edu
Office hours: Tuesdays, 1:30pm–3:00pm; Thursdays, 9:30am–11:00am; or by appointment
Office location: Woodruff Memorial Research Building, 4119
Teaching assistant
Name: Hejie Cui
Email address: hejie.cui@emory.edu
Office hours: Fridays, 1:00pm–4:00pm
Office location: Mathematics and Science Center E308 (The Computer Lab)
Prerequisites
Previous coursework in multivariate calculus, linear algebra, probability theory or statistics, and machine learning (CS534 or equivalent). Proficiency with numerical computing in Python and mathematical typesetting in LaTeX.
Grading
Students will be evaluated on periodic homework (30% of the course grade, lowest dropped) and quizzes (10%, lowest dropped), presentations and discussions of papers (30%), and a semester project (30%).
For the semester projects, each student (or each pair of students) will present a 3-minute “lightning” talk about their project during class on either Wednesday, April 22 or Monday, April 27 and submit a 4-page paper about their by 4:00pm on Monday, April 27. The project rubric describes the expectations for the project presentations and papers. The slides and paper must be submitted on this webpage or by email before Tuesday, April 28 at 9:00am ET.
Talks should highlight the background, goals, achievements, and challenges of the project. (Shorter talks are often harder to prepare than longer ones.) Papers should describe the project at greater length and provide adequate details about the methods and results. Papers must use the IEEE conference template in LaTeX and be between 4 and 4.5 pages long including the title, author list, and abstract but excluding the references. (Shorter papers are often harder to write than longer ones.) Previous guidance from homeworks and paper presentation will be helpful, and questions are welcome.
Lectures
This schedule is subject to change.
Home ■ People ■ Projects ■ Publications ■ Teaching