Instructors: Varsha Kishore and Justin Lovelace

Contact: or

Office hours:
Varsha Kishore: 1:00-2:00pm on Tuesdays in Rhodes 406
Justin Lovelace: 9:00-10:00am on Thursdays in Rhodes 404

Lectures: Tuesday and Thursday from 2:55 to 4:10 pm.

Course staff office hours:
Anissa Dallmann: Mondays 4:30-5:30pm in Rhodes 400
Adhitya Polavaram: Mondays 5:30-6:30pm in Rhodes 412
Zach Ross: Wednesdays from 4:00-5:00pm in Statler 453
Dylan Van Bramer: Fridays from 11:30-12:30pm in Rhodes 400
Luke Kulm: Fridays from 12:10-1:00pm in Rhodes 574

Course overview: This class is an introductory course to deep learning. It covers the fundamental principles behind training and inference of deep networks, deep reinforcement learning, the specific architecture design choices applicable for different data modalities, discriminative and generative settings, and the ethical and societal implications of such models.

Prerequisites: Fundamentals of Machine Learning (CS4780 , ECE4200 , STCSI4740), Python fluency (CS1110), and programming proficiency (e.g. CS 2110).

Course logistics: For enrolled students the companion Canvas page serves as a hub for access to Ed Discussions (the course forum) and Gradescope (for HWs). If you are enrolled in the course you should automatically have access to the site. Please let us know if you are unable to access it.

Homework, projects, and exams

Your grade in this course is comprised of four components: homework, mid-term exam, project and participation.


Final grades are based on homework assignments, project, exam and participation.

Your final grade consists of:


A tentative schedule is given below. It is quite possible the specific topics covered on a given day will change slightly. This schedule will be updated as necessary.

Topic Date Lecture References Notes/assignments
Week 1 Basics Jan 23 Logistics + History Slides
Jan 25 Multi-Layer Perceptrons (MLPs); Backpropagation DiDL (Ch. 4-5); CS 4780 (Sp2023);
Backprop; Tensorflow Playground
Week 2 Training Neural Networks Jan 30 Optimization DiDL (Ch. 12) Slides
Feb 1 Regularization DiDL (Ch. 3.7, 5.6, 8.5) Slides; HW1 Released
Week 3 Computer Vision Feb 6 Convolutional Neural Networks DiDL (Ch. 7) Slides
Feb 8 Modern ConvNets DiDL (Ch. 8) Slides
Week 4 Natural Language Processing Feb 13 Word Embeddings DiDL (Ch. 9) Slides
Feb 15 Recurrent Neural Networks (RNNs) Slides;HW1 Due; HW2 Released
Week 5 Feb 20 Attention; Transformers DiDL (Ch. 11) Slides
Feb 22 Large Language Models (LLMs) Speech and Language Processing (Chp. 10-11) Slides
Week 6 Graphs Feb 27 FEB BREAK (No Class)
Feb 29 Graph Neural Networks (GNNs) Slides;HW2 Due; HW3 Released
Week 7 Modern Vision Networks Mar 5 Vision Pre-Training (Supervised, Self-supervised) Slides
Mar 7 Vision-Language Models Slides;Project Proposal Due
Week 8 Generative Models Mar 12 Discriminators; Generative Adversarial Networks (GANs) Slides
Mar 14 U-Nets; Variational Autoencoders (VAEs) Slides;HW3 Due; HW4 Released
Week 9 Mar 19 Diffusion Models Slides
Mar 21 Diffusion II Slides
Week 10 Midterm Mar 26 Midterm Jeopardy HW4 Due (No new HW)
Mar 28 Midterm
Week 11 SPRING BREAK Apr 2 No Class
Apr 4 No Class
Week 12 Reinforcement Learning Apr 9 RL Setup Slides
Apr 11 Deep Q-Learning Slides;HW5 Released
Week 13 Apr 16 Policy Gradient Slides
Apr 18 RL w/ Human Feedback (RLHF) Slides
Week 14 AI in Human Society Apr 23 Robustness; Bias; AI Safety Slides
Apr 25 Interpretability; Legal Issues; Environmental Impacts Slides;HW5 Due; HW6 Released
Week 15 Final Projects Apr 30 Project Presentations
May 2 Project Presentations
Week 16 May 7 Project Presentations HW6 Due


While this course does not explicitly follow a specific textbook, there are useful references on many of the topics covered. Pointers to references will be provided here.

Background references


Course policies


You should expect and demand to be treated by your classmates and the course staff with respect. You belong here, and we are here to help you learn and enjoy this course. If any incident occurs that challenges this commitment to a supportive and inclusive environment, please let the instructors know so that the issue can be addressed. We are personally committed to this, and subscribe to the Computer Science Department’s Values of Inclusion. [Statement reproduced with permission from Dan Grossman.]

Mental health resources

Cornell University provides a comprehensive set of mental health resources and the student group Body Positive Cornell has put together a flyer outlined the resources available.


You are encouraged to actively participate in class. This can take the form of asking questions in class, responding to questions to the class, and actively asking/answering questions on the online discussion board.

Collaboration policy

Students are free to share code and ideas within their stated project/homework group for a given assignment, but should not discuss details about an assignment with individuals outside their group. The midterm and final exam are individual assignments and must be completed by yourself.

Academic integrity

The Cornell Code of Academic Integrity applies to this course.


In compliance with the Cornell University policy and equal access laws, we are available to discuss appropriate academic accommodations that may be required for student with disabilities. Requests for academic accommodations are to be made during the first three weeks of the semester, except for unusual circumstances, so arrangements can be made. Students are encouraged to register with Student Disability Services to verify their eligibility for appropriate accommodations.