Syllabus: CS 5043: Advanced Machine Learning (Spring 2018)
Machine learning is the data-driven process of constructing mathematical models
that can be predictive of data observed in the future.
In this course, we will study a range of
supervised and semi-supervised methods to solve both
classification and regression problems. In particular, we will focus
on methods that can robustly address data that are non-linear, noisy,
heterogeneous and/or high-dimensional. We will also study methods for
evaluation of the resulting models. In our homework and project work,
we will make use of several
python-based tool kits, including Scikit-learn, TensorFlow and XG-Boost.
Topics will include:
- Decision trees: ensemble methods, random forests, and boosting
- Regression and combating overfitting: ridge regression,
Tikhonov regression, lasso, elastic nets, support vector
- Nonlinear dimensionality reduction: Kernel PCA, local linear
embedding, ISOmap, multidimensional scaling
- Semi-supervised learning
- Deep learning: regularization, convolutional neural networks,
recurrent neural networks, autoencoders
- Evaluation in ML: metrics, cross-validation, statistics, addressing the multiple comparisons problem
- Meeting time: Tu/Th 1:30-2:45
- Location: Carson 121
OR permission of the instructor.
Linear Algebra (Math 3333), AND
- Statistics (Math 4743 or Math 4753 or ENGR 3293 or ISE 3293), AND
- Artificial Intelligence (CS 4013/5013) OR Data Mining (CS 5593) OR Intelligent Data Analytics (ISE/DSA 5103) OR Machine Learning (CS 4033/5033).
- Reading Materials:
- Aurélien Géron (2017) Hands-On Machine Learning with Scikit-Learn and TensorFlow
(Concepts, Tools, and Techniques to Build Intelligent Systems), ISBN-13: 978-1491962299
- Various papers and other network resources.
Other key materials:
Course web page:
- We will also be making heavy use of Canvas
- Instructor: Dr. Andrew H. Fagg
- 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:
- Canvas: This class will also use Canvas, located at:
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, an
electronic mailing list, newsgroups, 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, the discussion group on Canvas 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.
of personal interest should be directed to email instead of to
the newsgroup, e.g. informing me of an extended
- 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
Note that Canvas can be configured so that it will
forward messages, discussion posts and announcements directly to
your email address.
- Final Project: Your final project will occupy the last
few weeks of the semester on a topic of your choosing. You
will be responsible for an oral presentation for the proposal
and a checkpoint, and for a poster presentation on the last day
of the semester.
- Proper Academic Conduct:
- Homework assignments must be your own work.
- The project will be performed in small groups. Each group
member is expected to contribute equally to the final
- In all cases: while the net may be used as a reference,
downloading code from the net is prohibited.
Note that 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 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:
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.
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
- Making physical or verbal threats to a faculty member,
teaching assistant, or class member.
- Refusal to comply with faculty direction.
Grades will be computed according to the following distribution:
- In-class participation: 10%
- Homework assignments (6): 40%
- Project work: 50%
General Grade Issues
- Grade questions: If you have a question about grading
(including assessment of points), you may address these during
office hours or email. Note that if you are asking me to
reconsider a grade, then I will likely re-examine the entire
project. 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.
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 uses 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
for commonly asked
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.
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 http://www.cs.ou.edu/~fagg/classes/mlfds/syllabus.html
Andrew H. Fagg
Last modified: Tue Jan 16 13:52:30 2018