Syllabus: CS/DSA 5970: Machine Learning Practice (Fall 2019)

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 the use of a range of supervised, semi-supervised and unsupervised 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 assignments, we will make use of several Python-based tool kits, including Scikit-Learn and Pandas.

Topics

Topics will include:


General Information


Course Policies


Grades

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 https://www.ou.edu/eoo/faqs/pregnancy-faqs.html 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.

Registration and Withdrawal

If you choose to withdraw from this course, you must complete the appropriate University form and turn the form in before the deadline. If you stop attending the course and doing the coursework without doing the required paperwork, your grade will be calculated with missed homework and examination grades entered as zero. This could result in receiving a grade of F in the course. Deadlines are shown in the Academic Calendar, which is available from the Office of Admissions and Records or online at http://www.ou.edu/admissions/home/academic_calendar.html

Emergency Protocol

During an emergency, there are official university procedures that will maximize your safety.


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 http://www.cs.ou.edu/~fagg/classes/mlp/syllabus.html
Andrew H. Fagg
Last modified: Tue Aug 13 17:27:19 2019