[8/21/23] Please check announcements from iCollege.
[8/21/23] Please ask questions at Course Discussion List.
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Instructor: Jonathan Shihao Ji |
TA: Weizhen "Frank" Liu |
| Office: One Park Place, Room 637 |
Office: One Park Place, Room 625 |
| Office Hour: Mon. 3-4pm |
Office Hours: TT 1-2pm |
| Email: TBD |
Email: TBD |
Lecture location: Langdale Hall 227
Lecture times: MW 10:00 - 11:45 AM
About the Course
This course introduces the basic concepts and algorithms of deep neural networks (DNNs) and its applications to
computer vision and natural language processing. DNN architectures, such as MLP, CNN, LSTM, Transformer, etc., will be presented. Supervised
learning and unsupervised (or self-supervised) learning will be discussed. Depending on the course progress, selected topics such as model compression will be covered. The class emphasizes the understanding of the state-of-the-art DL architectures as well as practical implementations of DNNs with Python.
Textbook
Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press, 2016
Prerequisites
- This should not be your first machine learning class! You should have taken CSC 4740 Data Mining or CSC 4980 Machine Learning with a B or above.
- Basic knowledge in calculus, linear algebra, and statistics
- Proficiency in Python: All HWs will be in Python
Grading
| Participation |
10% |
| Assignments |
40% |
| Final Exam |
20% |
| Project |
30% |
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| A+ [97, 100]
| A [93, 97)
| A- [90, 93)
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| B+ [87, 90) |
B [83, 87) |
B- [80, 83) |
| C+ [75, 80) |
C [70, 75) |
| D [60, 70) |
F [0, 60) |
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- No late submissions accepted! All the assignment and project deadlines are 11:59 pm (EST) of the due dates.
- If you have doubts in your grading, please email TA and CC to the Instructor indicating the reason why you think it should be regraded.
- The regrading request should be submitted within 1 week after you receive your score.
Misc
- Course materials, assignments, projects and QAs are managed by iCollege.
Academic Honesty Policy
All work submitted for grading must be the student’s own work. A student who submits an assignment that copies the work of another student, in whole or in part, will be assigned a grade of zero for that assignment. Any student found to be cheating on an examination will receive a score of zero for that exam. Cheating on an assignment or exam may result in dismissal from the course and notification of the Dean of Students. More details on academic honesty can be found here.
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