Teaching

Spring 2019

EE381V Spl Topics in Machine Learning

Unique 16725, TuTh 12:30-2pm. ECJ 1.312
Pre-reqs: 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.

Fall 2018

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.

Spring 2018

EE380L Data Mining

Unique 15990, TuTh 12:30-2pm. ECJ 1.312
Pre-reqs: 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

EE380L1V Advanced Data Mining (F15)

This edition of the course focussed on Big Data Analytics for Healthcare.