Syllabus: CS 5043: Advanced Machine Learning:
Deep Learning (Spring 2025)

Topics

• Backpropagation
• Tensorflow and Keras
• Convolutional Neural Networks
• Recurrent Neural Networks
• Timeseries Processing
• Transformer Networks
• Semantic Segmentation
• Generative Models
• Model Interpretation
• ML experiment design and evaluation
• Using a supercomputer for ML experiments

Expectations and Teaching Approach

General Information

• Meeting time: Tu/Th 3:00 - 4:15

• Location: Devon Energy Hall 130

• Prerequisites:
  • Linear Algebra (Math 3333),
  • A statistics course from the departmentally approved list, AND
  • Machine Learning (CS 4033/5033) OR Neural Networks and Evolution (CS 5073)
  OR permission of the instructor.

• Reading Materials:
  • Main Textbook: Simon J.D. Prince (2023) Understanding Deep Learning, ISBN-13: 978-0262048644, MIT Press

  • Various papers and other network resources.

• Other key materials:
  • Class schedule: http://www.cs.ou.edu/~fagg/classes/aml/schedule.html
  • Scikit-Learn: http://scikit-learn.org/stable/
  • TensorFlow: https://www.tensorflow.org/api_docs/python/tf
  • Keras: https://keras.io/api/
  • Supercomputer Systems: https://www.ou.edu/oscer/getting_started/getting_started_using_oscer

• Course web page: https://symbiotic-computing.org/fagg_html/classes/aml_2025

• We will also be making heavy use of Canvas

• Discussions will be held on Slack (invitation is on the Syllabus section of Canvas)

• Instructor: Dr. Andrew H. Fagg
  • Office: DEH 243
  • Homepage: https://symbiotic-computing.org/fagg_html
  • Email: andrewhfagg--gmail.com
  • Office hours: see the office hours web page https://symbiotic-computing.org/fagg_html/office.html

• Teaching Assistant: Andrew Justin
  • Email: andrewjustin -- ou.edu
  • Office hours: see the office hours web page https://symbiotic-computing.org/fagg_html/office.html

Course Policies

• Attendance: This is a very discussion-oriented course. While keeping up with the readings is an important step to take, it is not a substitute for attending class.

• Class Web Page: Most of the material that you will need can be found on the class web page located at: https://symbiotic-computing.org/fagg_html/classes/aml

• Canvas: This class will also use Canvas, located at: http://canvas.ou.edu

  Login with your 4+4 (typically the first four letters of your last name followed by the last four digits of your student number), using your standard OU password. If you have difficulty logging in, call 325-HELP. This software provides a number of useful features, including a list of assignments and announcements, and a grade book.

  I may update the main web site and the Canvas page several times a week. When I update the site in any significant way, I will post an announcement on Canvas telling you what has been added and where it is located. You are responsible for things posted on the site within 48 hours of the post.

• Class Communication:
  • The class period will be a mixture of lecture and in-class exercises. Your active participation in both will result in a more salient experience.

  • Outside of class, Slack should be the primary method of communication. This allows everyone in the class to benefit from the answers to your questions, and provides students with more timely answers.

  • Matters of personal interest should be directed to email instead of to Slack, e.g. informing me of an extended illness.

  • Announcements will be posted to the Canvas announcement board. It is your responsibility to have Canvas configured so that you receive these messages in a timely fashion. Note that Canvas can be configured so that it will forward messages, discussion posts and announcements directly to your email address.

• Proper Academic Conduct:
  • Homework assignments must be your own work. While you may discuss with your peers the generalities of the assignments and solution paths, you may not exchange or look at the code solutions of your peers for the homework problems.

  • While the net may be used as a reference, downloading code that solves all or a substantial portion of any assignment from the net is prohibited.

  • Tools based on Large Language Models (e.g., chatgpt, bing chat, and alpaca) can be good sources of ideas, as well as detailed code examples. However, their output should always be treated skeptically, as these tools rely entirely on identifying common patterns across large text and code bases. You should always take the time to understand the suggestions that are being made and confirm that any code actually accomplishes what is needed. Nonetheless, using these services to solve all or a substantial portion of any assignment is prohibited.

  • Programs will be checked by software designed to detect improper copying. This software is extremely effective and has withstood repeated reviews by the campus judicial processes.

• Incompletes: The grade of "I" is intended for the rare circumstance when a student who has been successful in a class has an unexpected event occur shortly before the end of the class. I will not consider giving a student a grade of "I" unless the following three conditions have been met:
  • It is within two weeks of the end of the semester.
  • The student has a grade of C or better in the class.
  • The reason that the student cannot complete the class is properly documented and compelling.

• Classroom Conduct: Because cell phones and laptops can distract substantially from the classroom experience, students are asked not to use either during class, except in cases in which the laptop is required as part of a classroom exercise.

  Disruptions of class will also not be permitted. Examples of disruptive behavior include:

  • Allowing a cell phone or pager to repeatedly beep audibly.
  • Playing music or computer games during class in such a way that they are visible or audible to other class members.
  • Exhibiting erratic or irrational behavior.
  • Behavior that distracts the class from the subject matter or discussion.
  • Making physical or verbal threats to a faculty member, teaching assistant, or class member.
  • Refusal to comply with faculty direction.

  In the case of disruptive behavior, I may ask that you leave the classroom and may charge you with a violation of the Student Code of Responsibilities and Conduct.

Grades

• Homework assignments (total 9): 85%
• In-class exercises: 15%

Homework assignments are due on the date indicated on the semester schedule at 11:59pm. Assignments handed in late will incur a penalty (0-24 hours: 10%; 24-48 hours: 20%). Submissions will not be accepted after 48 hours.

General Grade Issues

• Grade questions: If you have a question about grading (including assessment of points), you may address these during office hours or in email. Note that if you are asking me to reconsider a grade, then I will likely re-examine the entire assignment. You have one week from the point that you receive feedback to address grading questions.

• Canvas Grade Summary: Canvas has a grade book that is used to store the raw data that is used to calculate your course grade. It is the responsibility of each student in this class to check their grades on Canvas after each assignment is graded. If an error is found, please bring it to my attention.

Course Evaluations

University Policies

Mental Health Support Services

Title IX Resources and Reporting Requirement

Because the University of Oklahoma is committed to the safety of you and other students, and because of our Title IX obligations, I, as well as other faculty, Graduate Assistants, and Teaching Assistants, are mandatory reporters. This means that we are obligated to report gender-based violence that has been disclosed to us to the Institutional Equity Office. This means that we are obligated to report gender-based violence that has been disclosed to us to the Institutional Equity Office. This includes disclosures that occur in: class discussion, writing assignments, discussion boards, emails and during Student/Office Hours. You may also choose to report directly to the Institutional Equity Office. After a report is filed, the Title IX Coordinator will reach out to provide resources, support, and information and the reported information will remain private. For more information regarding the University's Title IX Grievance procedures, reporting, or support measures, please visit  the Institutional Equity Office at 405-325-3546.

Reasonable Accommodation Policy

If you have not yet established services through ADRC, but have a documented disability and require accommodations, please complete the ADRC's pre-registration form to begin the registration process.  ADRC facilitates the interactive process that establishes reasonable accommodations for students at OU.  For more information on ADRC registration procedures, please review their Register with the ADRC web page.  You may also contact them at (405)325-3852 or adrc@ou.edu, or visit www.ou.edu/adrc for more information.

Note: disabilities may include, but are not limited to, mental health, chronic health, physical, vision, hearing, learning and attention disabilities, pregnancy-related.  ADRC can also support students experiencing temporary medical conditions.

Religious Observance

Adjustments for Pregnancy/Childbirth Related Issues

Final Exam Preparation Period

Emergency Protocols

Severe Weather: If you receive an OU Alert to seek refuge or hear a tornado siren that signals severe weather

1. LOOK for severe weather refuge location maps located inside most OU buildings near the entrances.
2. SEEK refuge inside a building. Do not leave one building to seek shelter in another building that you deem safer. If outside, get into the nearest building.
3. GO to the building's severe weather refuge location. If you do not know where that is, go to the lowest level possible and seek refuge in an innermost room. Avoid outside doors and windows.
4. GET IN, GET DOWN, COVER UP.
5. WAIT for official notice to resume normal activities.

Additional  Weather Safety Information  is available through the Department of Campus Safety.

The University of Oklahoma Active Threat Guidance

RUN: Running away from the threat is usually the best option. If it is safe to run, run as far away from the threat as possible. Call 911 when you are in a safe location and let them know from which OU campus you're calling from and location of active threat.

HIDE: If running is not practical, the next best option is to hide. Lock and barricade all doors; turn of all lights; turn down your phone's volume; search for improvised weapons; hide behind solid objects and walls; and hide yourself completely and stay quiet. Remain in place until law enforcement arrives. Be patient and remain hidden.

FIGHT: If you are unable to run or hide, the last best option is to fight. Have one or more improvised weapons with you and be prepared to attack. Attack them when they are least expecting it and hit them where it hurts most: the face (specifically eyes, nose, and ears), the throat, the diaphragm (solar plexus), and the groin.

Please save OUPD's contact information in your phone:

• NORMAN campus: For non-emergencies call (405) 325-1717. For emergencies call (405) 325-1911 or dial 911.

• TULSA campus: For non-emergencies call (918) 660-3900. For emergencies call (918) 660-3333 or dial 911.

Fire Alarm/General Emergency

1. LEAVE the building. Do not use the elevators.
2. KNOW at least two building exits
3. ASSIST those that may need help
4. PROCEED to the emergency assembly area
5. ONCE safely outside, NOTIFY first responders of anyone that may still be inside building due to mobility issues.
6. WAIT for official notice before attempting to re-enter the building.

Links: OU Fire Safety on Campus

Copyright notice: Many of the materials created for this course are the intellectual property of Andrew H. Fagg. This includes, but is not limited to, the syllabus, lectures and course notes. Except to the extent not protected by copyright law, any sale of such materials requires the permission of the instructor.