EE461P Data Science Principles
TuTh 11am-12:30pm. Online
This course is meant for senior/advanced-junior undergrads in ECE with adequate background in math/stats and programming (see pre-requisites). Graduate students are not allowed; they should instead consider EE380L: Data Mining.
EE380L Data Mining
Unique 16630, TuTh 12:30-2pm. ECJ 1.318.
This course is for graduate students. ECE undergrads should take EE461P instead. Also, due to anticipated demand, pre-enrollment is restricted to ECE graduate students till the first day of class, when other qualified (see pre-reqs in the course descriptor linked above) are welcome to enroll.
If the class is full but you are really interested, do persist and be on the wait list; typically spots open after the first week of classes.
MIS 382N: ADVANCED PREDICTIVE MODELING - MSBA (Unique 04220/225/230)
This is not a regular course but an "Option III" course. Hence all sections of this course are restricted to students in the McCombs "Masters in Business Analytics" (MSBA/MSF) Programs. Other UT students (including ECE students) cannot be enrolled for this course.
Previously offered Courses include
EE381V FAT ML: Spl Topics in Machine Learning (Spring 2019)
: At least one graduate course completed in Data Mining/Machine Learning. Online courses do not count.
This is an advanced, seminar-oriented course. We shall study recently published papers relevant to the development of responsible and trustworthy data driven automated decision systems. Solid background in pattern recognition/machine learning is assumed. Key topics include building explainable ML models, black-box explainability, algorithmic fairness, adversarial ML, robust statistical modeling, and privacy aware data mining. Coursework will mainly involve paper presentations, critiques and discussion, a mini coding-based project and a major term project on developing some aspects of a responsible ML system.
Course READING LIST
This edition of the course focussed on Big Data Analytics for Healthcare.