EE381V Spl Topics in Machine Learning
Unique 16725, TuTh 12:30-2pm. ECJ 1.312
: 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.
EE461P Data Science Principles (16720)
TuTh 11am-12:30pm, EER 1.516
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, which is tentatively scheduled for Spring 2019.
MIS 382N: ADVANCED PREDICTIVE MODELING - MSBA (04070)
TuTh 12:30pm-2pm, GSB 3.104
This course is restricted to students in the McCombs "Masters in Business Analytics" (MSBA) Program. Other UT students cannot be enrolled for this course.
EE380L Data Mining
Unique 15990, TuTh 12:30-2pm. ECJ 1.312
: Graduate standing in Engineering, CS, Maths or Physics) OR (consent of the instructor). You are expected to know basics (undergraduate level) of probability/statistics. Knowledge of basic linear algebra and algorithms will be assumed. Basic knowledge of Python (or ability to pick it up on your own) is also assumed.
Previously offered Courses include
This edition of the course focussed on Big Data Analytics for Healthcare.