Big Data Analytics in Healthcare (CSE 6250)
The course introduces students to analysis and visualization of complex high dimensional data. Both theory and applications will be covered including several practical case studies.
Database Systems Concepts and Design (CS 6400)
Study of fundamental concepts with regard to rational databases. Topics covered include database design, query processing, concurrency control, and recovery. Credit not given for both CS 6400 and CS 6754.
Artificial Intelligence (CS 6601)
Human Computer Interaction (CS 6750)
Knowledge-based AI (CS 7637)
Reinforcement Learning (CS 7642)
Deep Learning (CS 7643)
Machine Learning for Trading (CS 7646)
This course introduces students to the real-world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.
Time Series Analysis (ISYE 6402)
Basic forecasting and methods, ARIMA models, transfer functions.
Regression Analysis (ISYE 6414)
Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.
Bayesian Statistics (ISYE 6420)
This course covers the fundamentals of Bayesian statistics, including both the underlying models and methods of Bayesian computation, and how they are applied. Modeling topics include conditional probability and Bayes’ formula, Bayesian inference, credible sets, conjugate and noninformative priors, hypothesis testing, Bayesian regression, empirical Bayes models, and hierarchical Bayesian models. Computational topics include Monte Carlo methods, MCMC, Metropolis-Hasting algorithms, Gibbs sampling, variational Bayes, and other methods for posterior approximation. Various applications of Bayesian statistics will be discussed. Prerequisite: Calculus-based Introductory Statistics Course
Simulation (ISYE 6644)
Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.
Deterministic Optimization (ISYE 6669)
The course will teach basic concepts, models, and algorithms in linear optimization, integer optimization, and convex optimization. The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques, basic polyhedral theory, simplex method, and duality theory.
Computational Data Analysis (ISYE 6740)
Data Mining and Statistical Learning (ISYE 7406)
An introduction to some commonly used data mining and statistical learning algorithms such as K-nearest neighbor (KNN) algorithm, linear methods for regression and classification, tree-based methods, ensemble methods, support vector machine, neural networks, and Kmeans clustering algorithm. This course focuses on the understanding of methodology, motivation, and assumptions of different algorithms as well as implementation of these algorithms with data examples using the R statistical software.
Topics on High-dimensional Data Analytics (ISYE 8803)
Digital Marketing (MGT 6311)
Become familiar with the key concepts and techniques utilized in modern digital marketing. Understand the primary characteristics of various online channels including mobile marketing, email marketing, and social media marketing. Gain awareness of important concepts and best practices in the use of digital marketing tools (search engine optimization, pay-per-click advertising, etc.).
Financial Modeling (MGT 8813)
Students will create spreadsheets using pivot tables, Excel functions, solver, goal seek, and VBA. The course will also include topics such as time value of money, stock and bond valuation, firm valuation, financial statements, cost of capital, option pricing models, and portfolio optimization. This course is intended to prepare students to build financial models.
Data Analysis for Continuous Improvement (MGT 8823)
Privacy for Professionals (MGT 8833)