Meet Time/Location: T/R 09:40-10:55 in Morey 321
Instructor: Prof. Chenliang Xu
Instructor's Office Hours: Tue 2:00-3:00 pm in Wegmans 3005
TAs and Office Hours:
Information Flow: We will use Blackboard to make announcements, host lecture notes/assignments, and submit/grade student work. The course ID is CSC245.01.FALL2026UROCH. We will use Blackboard's Discussions Tab for class-related discussion and communication, so that you can get help fast from the teaching team and your fellow classmates. Don't hensitate to email me and the TAs if it is a personal matter. Finally, this course website containing the course schedule serves as a syllabus.
Credit Hours: This is a four-credit-hour course. The credit hours are met by weekly lectures (150 minutes), office hours, homework assignments, and a final course project.
Much of the recent advances in machine learning and artificial intelligence have been dominated by neural network approaches broadly described as deep learning. This course provides an overview of the most important deep learning techniques covering both theoretical foundations and practical applications. The applications focus on problems in image understanding and language modeling utilizing state-of-the-art deep learning libraries and tools, which will be introduced in the course.
Course Objective: The course aims to provide students with a dive to deep learning foundations and an introduction to modern research topics in this field. It is designed as an upper-level elective course for junior and senior students in CS and DCS and an AI breath course for CS graduate students.
Prerequisites: MTH 164 (Multivariable Calculus) or MTH 165 (linear algebra), and CSC 172 (data structures and algorithms). Programming in Python. Recommended but not required: CSC 242.
CS Instructional Network: The course programming assignments require you to have an account in the CS instructional network. You should test your code in the CS instructional network before submission as this is the environment we use to grade assignments. Follow this link to request an account if you don't have one.
Below is a tentative schedule of the course and is subject to changes over the term.
Part I: Neural Network Basics,
Part II: Intermediate Topics,
Part III: Advanced Topics.
| Dates | Tuesday (T) | Thursday (R) | Final Project |
|---|---|---|---|
| 9/1,3 | Introduction I | Introduction II | |
| 9/8,10 | Machine Learning Basics I | Machine Learning Basics II | |
| 9/15,17 | Feedforward Neural Networks | Programming Tutorial | |
| 9/22,24 | Backpropagation | Optimization | |
| 9/29,10/1 | Regularization | Convolutional Neural Networks I | |
| 10/6,8 | Teaming & Brainstorming | Convolutional Neural Networks II | Teaming Due |
| 10/13,15 | Fall Break | Explain and Visualization | |
| 10/20,22 | Recurrent Neural Networks | Graph Neural Networks | Proposal |
| 10/27,29 | Transformers I | Transformers II | |
| 11/3,5 | Large Language Models I | Large Language Models II | |
| 11/10,12 | Mid-term Exam | Multimodal Learning | |
| 11/17,19 | Generative Models I | Generative Models II | |
| 11/24,26 | Trustworthy AI | Thanksgiving | |
| 12/1,3 | Something Interesting | Something Interesting | |
| 12/8,10 | Project Presentation I | Project Presentation II | Final Report |
Regrading: Any questions about the grading of a piece of work must be raised within one week of the date that the work was returned. In other words, if you do not pick up your work in a timely fashion, you may forfeit your right to question the grading of your work.
I do not enforce a textbook. However, you may find the following textbooks very helpful:
There are tons of online materials and courses for learning deep learning. This Github repository awesome-deep-learning, although not up-to-date, is an excellent place to get started. You may also learn from deep learning library tutorials and manuals. However, technologies come and technologies go, but insights stay. Therefore, it is more important to understand the principles behind, and you can quickly master any libraries later if needed.
Academic Honesty: All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available here. You are expected to read, understand, and follow the policy.
AI Usage: AI can be a powerful tool for both learning and productivity. You are allowed to use any AI tool in this course, provided that you clearly acknowledge its use and explain how you used it. See the APA Style Guide for how to cite AI generated content. Failing to cite verbatim quotes or paraphrased material from a Generative AI tool is considered plagiarism. Note, too, that you are still responsible for the accuracy of the content you submit for this course. Submission of inaccurate or hallucinated content may be considered a form of fabrication and could result in an academic honesty case being filed. If you have any questions about a particular tool or how to use it for this class, please talk to me.
Disabilities: Please see me about your required accommodations as early as possible in the term. The University of Rochester respects and welcomes students of all backgrounds and abilities. In the event you encounter any barrier(s) to full participation in this course due to the impact of a disability, please contact the Office of Disability Resources. The access coordinators in the Office of Disability Resources can meet with you to discuss the barriers you are experiencing and explain the eligibility process for establishing academic accommodations. You can reach the Office of Disability Resources at: disability@rochester.edu; (585) 276-5075; Taylor Hall; www.rochester.edu/college/disability.