EE380L Data Mining
Unique 170200, TuTh 12:30-2pm. ECJ 1.302.
This course is for graduate students. ECE undergrads should take EE461P instead.
One cannot get credit for both EE461P and EE380L due to the high degee of overlap.
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 adequate number of spots open after the first week of classes.
MIS 382N: ADVANCED MACHINE LEARNING - MSBA (Unique 04580/85/90)
MW 9:30am, GSB3.106; 4:30-6pm GSB 3.130.
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.
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.
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.