• Traffic Operations

    This course provides students with fundamental knowledge of traffic operations including traffic data collection and analysis, safety and crash studies, traffic flow theory, highway capacity analysis, signalized intersection design and analysis, simulation modeling, and sustainable transportation system.

  • Introduction To Nlp

    This is an introductory course in natural language processing (NLP). It explores a broad set of NLP tasks and introduces the students to the data, methods, and baseline solutions related to each. Topics covered include n-gram language models, text classification, part of speech tagging, word sense disambiguation, named entity extraction, information retrieval, and question answering.…

  • Machine Learning In Image Analysis

    This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed to build their own models for effective problem solving. The graduate students (ECE/CS 6501) will be given additional programming tasks and more advanced theoretical questions.

  • Industrial Control Sys Security

    Provides an introduction to industrial control systems (ICS) at a Graduate level. Covers fundamental concepts of control loop and its main components. Human-Machine Interface (HMI) and displays. Remote measurements through networks or through telemetry. Diagnostic and maintenance utilities. Input-Output servers. Data historian utility. SCADA systems. ICS Security. Connectivity of the control system network to other…

  • Intro. To Computer Vision

    Overview of digital image processing including visual perception, image formation, spatial transformations, image enhancement, color image representation and processing, edge detection, image segmentation, and data processing method for computer vision applications. Hand-on projects will be introduced to better understand computer vision applications.

  • Modern Telecommunication

    Comprehensive overview of telecommunications, including current status and future directions. Topics include review of evolution of telecommunications; voice and data services; basics of signals and noise, digital transmission, network architecture and protocols; local area, metropolitan and wide area networks and narrow band ISDN; asynchronous transfer mode and broadband ISDN; and satellite systems, optical communications, cellular…

  • Bayesian Inference/dec Theory

    Introduces decision theory and relationship to Bayesian statistical inference. Teaches commonalities, differences between Bayesian and frequentist approaches to statistical inference, how to approach statistics problem, and how to combine data with informed expert judgment to derive useful and policy relevant conclusions. Teaches theory to develop understanding of when and how to apply Bayesian and frequentist…

  • Applied Statistical Learning

    Tools for the analysis of massive data sets. Topics include: regression, classification trees, clustering, and support vector machines. Extensive use of statistical software. Applications to business, finance, biology, and other sciences and engineering. Notes: Students may not receive credit for both STAT 472 and STAT 572. Cannot be used to satisfy requirements for MS in Statistical Science without prior written…

  • Info.science For Systems And Engineering Management

    This course aims to prepare students with the general knowledge and skills for the on-going digital transformation. The course covers: (1) preliminaries of information and informatics; (2) information and knowledge modeling; (3) fundamental concepts, models, tools, and applications of Big Data; and (4) digital mechanisms of trust and security, including: digital asset access control, digital…

  • Multivariate Ststistics For Engineering

    Introduction to modeling multivariate structural and residual variation, using exploratory data analysis, nonparametric regression, dependence regression, and factor analytic models, with a goal of producing robust, generalizable multivariate models that support research findings. Statistical analyses will be performed in the free general public licensed R statistical software with references to Minitab and SPSS.