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:

General Information

Course Policies


Grades will be computed according to the following distribution:

General Grade Issues

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 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 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.

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.

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Andrew H. Fagg
Last modified: Tue Jan 16 13:52:30 2018