Syllabus: CS 5043: Advanced Machine Learning:
Deep Learning (Spring 2024)
Machine learning is the data-driven process of constructing
mathematical models that can 1) make predictions about future
situations, or 2) take actions in a future situation to optimize some
outcome. Neural Networks (one form of ML method) are
unstructured and expressive models that can be used for function
approximation and classification. In this course, we will study a
range of Deep Learning tools that allow for the efficient
construction of very complex Neural Network models.
We will also study methods for model evaluation. In
our homework work,
we will make use of several python-based tools, including
Tensorflow and Keras.
Topics
Topics include:
- 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
General Information
- Meeting time: Tu/Th 3:00 - 4:15
- Location: Devon Energy Hall 130
- Prerequisites:
-
Linear Algebra (Math 3333), AND
- Statistics (Math 4743 or Math 4753 or ENGR 3293 or ISE 3293), 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:
-
Course web page:
https://symbiotic-computing.org/fagg_html/classes/aml_2024
- 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
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
collaborative project work. 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.
- Services 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.
-
Accommodation of Disabilities: The University of Oklahoma is committed to providing
reasonable accommodation for all students with disabilities. Students with disabilities who
require accommodations in this course are requested to speak with the professor as early in the
semester as possible. Students with disabilities must be registered with the Office of Disability
Services prior to receiving accommodations in this course. The
Office of Accessibility and Disability Services is
located in Goddard Health Center, Suite 166, phone 405/325-3852 or TDD only 405/325-4173.
- 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
Grades will be computed according to the following distribution:
- 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
The College of Engineering utilizes student ratings as one of the
bases for evaluating the teaching effectiveness of each of its faculty
members. The results of these forms are important data used in the
process of awarding tenure, making promotions, and giving salary
increases. In addition, the faculty use these forms to improve their
own teaching effectiveness. The original request for the use of these
forms came from students, and it is students who eventually benefit
most from their use. Please take this task seriously and respond as
honestly and precisely as possible, both to the machine-scored items
and to the open-ended questions.
Adjustments for Pregnancy/Childbirth Related Issues
Should you need
modifications or adjustments to your course requirements
because of documented pregnancy-related or childbirth-related
issues, please contact me as soon as possible to
discuss. Generally, modifications will be made where medically
necessary and similar in scope to accommodations based on
temporary disability. Please see
https://www.ou.edu/content/dam/eoo/documents/faqs/faqs-pregnant-and-parenting-students.pdf
for commonly asked
questions.
Title IX Resources
For any concerns regarding gender-based
discrimination, sexual harassment, sexual misconduct, stalking,
or intimate partner violence, the University offers a variety
of resources, including advocates on-call 24.7, counseling
services, mutual no contact orders, scheduling adjustments and
disciplinary sanctions against the perpetrator. Please contact
the Sexual Misconduct Office 405-325-2215 (8-5) or the Sexual
Assault Response Team 405-615-0013 (24.7) to learn more or to
report an incident.
Religious Observance
It is the policy of the University to excuse the absences of students
that result from religious observances and to reschedule examinations
and additional required classwork that may fall on religious holidays,
without penalty.
[See Faculty Handbook 3.15.2] . Students who will be observing a
religious holiday must contact the instructor ahead of time to arrange
a plan.
Final Exam Preparation Period
Pre-finals week is defined as the seven calendar days before the
first day of finals. Faculty may cover new course material throughout
this week. For specific provisions of the policy please refer to OU's
Final Exam Preparation Period Policy.
Emergency Protocols
During an emergency, there are official university procedures that
will maximize your safety.
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.
Links: Severe Weather Refuge Areas, Severe Weather Preparedness
Fire Alarm/General Emergency: If you receive an OU Alert that
there is danger inside or near the building, or the fire alarm inside
the building activates: 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
Armed Subject/Campus Intruder:
(it is sad that this must be included in our syllabi)
If you receive an OU Alert to
shelter-in-place due to an active shooter or armed intruder situation
or you hear what you perceive to be gunshots: 1. GET OUT: If you
believe you can get out of the area WITHOUT encountering the armed
individual, move quickly towards the nearest building exit, move away
from the building, and call 911. 2. HIDE OUT: If you cannot flee, move
to an area that can be locked or barricaded, turn off lights, silence
devices, spread out, and formulate a plan of attack if the shooter
enters the room. 3. TAKE OUT: As a last resort fight to defend
yourself.
Links:
Responding
to Gunshots
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.
This page is online at https://symbiotic-computing.org/fagg_html/classes/aml/syllabus.html
Andrew H. Fagg
Last modified: Mon Jan 15 22:18:23 2024