CAP4613 Deep Learning for Computer Graphics Spring 2022 


This course covers neural network architectures (deep learning), related math and the fundamental theory and application of AI algorithms most popular in the field of computer graphics. Programming assignments will help students develop graphics processing unit (GPU) programming skills while implementing concepts learned in lectures and readings using deep learning APIs on the UF HiPerGator NVIDIA cluster. Generative Adversarial Networks (GANs) for facial animation is a project example.


This undergraduate course covers the fundamental theory and application of AI algorithms in the context of computer graphics. The course begins with deep learning basics including several lectures that cover related math (numerical analysis, gradient optimization, eigen value decomposition and math for computer graphics). Students will then learn fundamental deep learning concepts including: supervised, unsupervised and reinforcement learning, neural network architectures (like convolutional neural networks, autoencoders and generative adversarial networks), backpropagation, training, testing and fine-tuning. Students will develop graphics processing unit (GPU) programming skills while implementing concepts learned for real computer graphics applications. Convolutional neural networks for colorizing black and white movies is an example. Students will complete programming assignments using the UF HiPerGator NVIDIA cluster. Weekly quizzes are designed to help students access their understanding of course material on a regular basis. These quizzes also provide preparation for the written homework and take-home exam which are designed to help students develop problem solving skills that use mathematical concepts covered in the course material.


Instructor: Dr. Corey Toler-Franklin, CISE Department, University of Florida

Contact: Office CSE 332 or  Lab CSE 319,

Office Hours: TBD

Location:  Zoom (See details in canvas)

Time: MWF Period 5 (11:45 am – 12:35 pm)

Course Management: Canvas




Proficiency in a programming Language (Python and/or C++ recommended), Data Structures and Algorithms, Linear Algebra, and Calculus.

**Contact instructor if you are not sure you are prepared for the course**




ISBN: 9780262035613

Author: Ian Goodfellow, Yoshua Bengio and Aaron Courville,

Title: Deep Learning

Free online version



CAP5404 Spring2022 Syllabus
course survey out
Machine Learning Basics
Goodfellow 5-5.2.0, 5.3
Machine Learning Basics
course survey due
Math: Numerical Analysis: Gradient Optimization
Goodfellow 4.3
14-Jan ***
HiPerGator Training
Martin Luther King, Jr. Day – NO CLASSES
Math: Numerical Analysis: Gradient Optimization
21-Jan ***
Math: Numerical Analysis: Gradient Optimization
Neural Networks
Goodfellow 6, 6.1, 6.4, 14, 14.1, 14.9
Proj 1. Classification/Regression
Neural Networks
Goodfellow 5.9, 6.5
28-Jan ***
Neural Networks
Goodfellow 7.12, 8.4, 8.7.1
Python, Torch, CUDA, cuDNN, TensorFlow
Python, Torch, CUDA, cuDNN, TensorFlow
4-Feb ***
Python, Torch, CUDA, cuDNN, TensorFlow
Training, Testing, Fine-tuning
Goodfellow 15.2, 7.4
Proj 2. CNNs for Graphics
Training, Testing, Fine-tuning
Proj 1. due
11-Feb ***
Convolutional Neural Networks
Goodfellow 9-9.3
Convolutional Neural Networks
Traditional Machine Learning
Toler-Franklin 2010
18-Feb ***
Final Project Discussion
Recurrent Neural Networks
Goodfellow 10-10.2.2, 10.10.1
Natural Language Processing
25-Feb ***
Math for Computer Graphics
Math for Computer Graphics
Proj 2. due
Math for Computer Graphics
Final Proj. Proposal due
4-Mar ***
Spring Break – NO CLASSES
Spring Break – NO CLASSES
Spring Break – NO CLASSES
Deep Learning – inverse graphics problem
Deep Learning in Graphics: Recent trends
written hw out
Deep Learning in Medicine
Zhang, Heldermon &Toler-Franklin 2020
Generative Adversarial Networks
Generative Adversarial Networks
Final Project Mid. Eval
Reinforcement Learning
written hw due
Take Home Exam
Take Home Exam out
Reinforcement Learning
Take Home Exam due
Image Synthesis (and relighting)
Song 2021
Image Denoising
Motion from Video
Karpathy 2014,
Learning from Physics
Learning from Physics
Autonomous Driving
TED Talk 1., Janai 2017
Autonomous Driving
TED Talk 2., Pinto 2017
Final Project due


All assignments are distributed and submitted in Canvas.

60% Programming projects

15%  Take Home Exam (1)

15%  Written Homework (1)

10%  Quizzes (~weekly)


Attendance Policy, Class Expectations, and Make-Up Policy
Attendance is expected and taken in class. One half of a letter grade will be deducted (e.g. an A becomes a B+) for missing more than 3 classes for the semester without a documented university excuse. Make-Up homework, projects exams will be coordinated with the instructor for university excused absences. Excused absences must be consistent with university policies in the undergraduate catalog ( and require appropriate documentation.


Grading Policy
93.4 – 100 A 4.00
90.0 – 93.3 A- 3.67
86.7 – 89.9 B+ 3.33
83.4 – 86.6 B 3.00
80.0 – 83.3 B- 2.67
76.7 – 79.9 C+ 2.33
73.4 – 76.6 C 2.00
70.0 – 73.3 C- 1.67
66.7 – 69.9 D+ 1.33
63.4 – 66.6 D 1.00
60.0 – 63.3 D- 0.67
0 – 59.9 E 0.00

More information on UF grading policy may be found at:


Students Requiring Accommodations
Students with disabilities who experience learning barriers and would like to request academic accommodations
should connect with the disability Resource Center by visiting It is
important for students to share their accommodation letter with their instructor and discuss their access needs, as
early as possible in the semester.
Course Evaluation
Students are expected to provide professional and respectful feedback on the quality of instruction in this course by
completing course evaluations online via GatorEvals. Guidance on how to give feedback in a professional and
respectful manner is available at Students will be notified when the
evaluation period opens, and can complete evaluations through the email they receive from GatorEvals, in their
Canvas course menu under GatorEvals, or via Summaries of course evaluation results
are available to students at


In-Class Recording
Students are allowed to record video or audio of class lectures. However, the purposes for which these recordings
may be used are strictly controlled. The only allowable purposes are (1) for personal educational use, (2) in
connection with a complaint to the university, or (3) as evidence in, or in preparation for, a criminal or civil
proceeding. All other purposes are prohibited. Specifically, students may not publish recorded lectures without the
written consent of the instructor.


A “class lecture” is an educational presentation intended to inform or teach enrolled students about a particular
subject, including any instructor-led discussions that form part of the presentation, and delivered by any instructor
hired or appointed by the University, or by a guest instructor, as part of a University of Florida course. A class
lecture does not include lab sessions, student presentations, clinical presentations such as patient history,
academic exercises involving solely student participation, assessments (quizzes, tests, exams), field trips, private
conversations between students in the class or between a student and the faculty or lecturer during a class session.
Publication without permission of the instructor is prohibited. To “publish” means to share, transmit, circulate,
distribute, or provide access to a recording, regardless of format or medium, to another person (or persons),
including but not limited to another student within the same class section. Additionally, a recording, or transcript
of a recording, is considered published if it is posted on or uploaded to, in whole or in part, any media platform,
including but not limited to social media, book, magazine, newspaper, leaflet, or third party note/tutoring services.
A student who publishes a recording without written consent may be subject to a civil cause of action instituted by
a person injured by the publication and/or discipline under UF Regulation 4.040 Student Honor Code and Student
Conduct Code.
University Honesty Policy
UF students are bound by The Honor Pledge which states, “We, the members of the University of Florida community,
pledge to hold ourselves and our peers to the highest standards of honor and integrity by abiding by the Honor Code.
On all work submitted for credit by students at the University of Florida, the following pledge is either required or
implied: “On my honor, I have neither given nor received unauthorized aid in doing this assignment.” The Conduct
Code ( specifies a number of behaviors that are in violation
of this code and the possible sanctions. If you have any questions or concerns, please consult with the instructor or
TAs in this class.


Commitment to a Safe and Inclusive Learning Environment

The Herbert Wertheim College of Engineering values broad diversity within our community and is committed to
individual and group empowerment, inclusion, and the elimination of discrimination. It is expected that every
person in this class will treat one another with dignity and respect regardless of gender, sexuality, disability, age,
socioeconomic status, ethnicity, race, and culture.
If you feel like your performance in class is being impacted by discrimination or harassment of any kind, please
contact your instructor or any of the following:
• Your academic advisor or Graduate Program Coordinator
• Jennifer Nappo, Director of Human Resources, 352-392-0904,
• Curtis Taylor, Associate Dean of Student Affairs, 352-392-2177,
• Toshikazu Nishida, Associate Dean of Academic Affairs, 352-392-0943,
Software Use
All faculty, staff, and students of the University are required and expected to obey the laws and legal agreements
governing software use. Failure to do so can lead to monetary damages and/or criminal penalties for the individual
violator. Because such violations are also against University policies and rules, disciplinary action will be taken as
appropriate. We, the members of the University of Florida community, pledge to uphold ourselves and our peers to
the highest standards of honesty and integrity.
Student Privacy
There are federal laws protecting your privacy with regards to grades earned in courses and on individual
assignments. For more information, please see:
Campus Resources:
Students will have access to a GPU cluster for completing the course project.
Health and Wellness
U Matter, We Care:
Your well-being is important to the University of Florida. The U Matter, We Care initiative is committed to
creating a culture of care on our campus by encouraging members of our community to look out for one another
and to reach out for help if a member of our community is in need. If you or a friend is in distress, please contact so that the U Matter, We Care Team can reach out to the student in distress. A nighttime and
weekend crisis counselor is available by phone at 352-392-1575. The U Matter, We Care Team can help connect
students to the many other helping resources available including, but not limited to, Victim Advocates, Housing
staff, and the Counseling and Wellness Center. Please remember that asking for help is a sign of strength. In case
of emergency, call 9-1-1.
Counseling and Wellness Center:, and 392-1575; and the University Police
Department: 392-1111 or 9-1-1 for emergencies.
Sexual Discrimination, Harassment, Assault, or Violence
If you or a friend has been subjected to sexual discrimination, sexual harassment, sexual assault, or violence
contact the Office of Title IX Compliance, located at Yon Hall Room 427, 1908 Stadium Road, (352) 273-1094,
Sexual Assault Recovery Services (SARS)
Student Health Care Center, 392-1161.
University Police Department at 392-1111 (or 9-1-1 for emergencies), or
• You are expected to wear approved face coverings at all times during class and within buildings even if
you are vaccinated.
• If you are sick, stay home and self-quarantine. Please visit the UF Health Screen, Test & Protect website
about next steps, retake the questionnaire and schedule your test for no sooner than 24 hours after your
symptoms began. Please call your primary care provider if you are ill and need immediate care or the UF
Student Health Care Center at 352-392-1161 (or email to be evaluated for testing and
to receive further instructions about returning to campus.
• If you are withheld from campus by the Department of Health through Screen, Test & Protect, you are not
permitted to use any on campus facilities. Students attempting to attend campus activities when withheld
from campus will be referred to the Dean of Students Office.
• UF Health Screen, Test & Protect offers guidance when you are sick, have been exposed to someone who
has tested positive or have tested positive yourself. Visit the UF Health Screen, Test & Protect website for
more information.
• Please continue to follow healthy habits, including best practices like frequent hand washing. Following
these practices is our responsibility as Gators.
Academic Resources
E-learning technical support, 352-392-4357 (select option 2) or e-mail to
Career Resource Center, Reitz Union, 392-1601. Career assistance and counseling;
Library Support, Various ways to receive assistance with respect to using the
libraries or finding resources.
Teaching Center, Broward Hall, 392-2010 or 392-6420. General study skills and tutoring.
Writing Studio, 302 Tigert Hall, 846-1138. Help brainstorming, formatting, and writing papers.
Student Complaints Campus:;
On-Line Students Complaints:

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