Data Science MS

Introduces concepts and skills fundamental to Data Science including: getting data, data wrangling, exploratory data analysis, basic statistics, data visualization, data modeling, and learning. The course introduces data science from different perspectives: computer science, mathematics, business, engineering, and more.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science

Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration

Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology (measurement, evaluation, visualization), algorithms (classification/regression, clustering, association rules) and applications (web mining, recommender systems, bioinformatics).

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): (CS 3425 or MIS 3100) and (MA 2330 or MA 2320 or MA 2321) and (MA 2710 or MA 2720 or MA 3710)

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)

This course provides an introduction to database systems including database design, query, and programming. Topics include goals of database management; data definition; data models; data normalization; data retrieval and manipulation with relational algebra and SQL; data security and integrity; database and Web programming; and languages for representing semi-structured data.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): (CS 2311 or MA 3210) and CS 2321

The fundamentals of GIS and its application to natural resource management. Spatial data, its uses and limitations are evaluated. Students work extensively with the ARCGIS software package.

• Credits: 4.0
• Lec-Rec-Lab: (3-0-3)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 2710(C) or MA 2720(C) or MA 3710(C) or ENVE 3502 or CEE 3502(C)

Introduction to the design, conduct, and analysis of statistical studies aimed at solving engineering problems. Topics include methods of data collection, descriptive and graphical methods, probability and probability models, statistical inference, control charts, linear regression, design of experiments.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Pre-Requisite(s): MA 2160 or MA 3160(C)

Project-based course enabling students to identify statistical methods and analysis using R and SAS. Topics include exploratory data analysis, classical statistical tests, sample size and power considerations, correlation, regression,and design experiments using advanced programming techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

Introduction to the design and analysis of statistical studies in the health and life sciences. Topics include study design, descriptive and graphical methods, probability, inference on means, categorical data analysis, and linear regression.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 1135 or MA 1160 or MA 1161 or MA 1121 or MA 2160(C) or MA 3160(C)

Emphasizes database principles that are constant across different database software products through concrete examples using a relational database management system. Provides a well-rounded business perspective about developing, utilizing, and managing organizational databases.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MIS 2000(C) or MIS 2100 or CS 1122 or CS 1131

Introductory course on database management. Topics include data modeling, database design, implementation techniques, SQL Language, database administration and security.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Class(es): Junior, Senior
• Pre-Requisite(s): SAT 1200 or CS 1111 or CS 1121 or CS 1131 or CS 1142 or MIS 2100

Administrating Linux and Microsoft servers together to provide infrastructure services to mixed clients. Topics include: DNS; DHCP; file, web, mail, and directory security of these services; and best practices for combining and mixing server platforms in an enterprise environment.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall
• Pre-Requisite(s): SAT 2711

This course introduces students to the Python programming language in applied computing systems and applications. In addition to Python basics, introduction to advanced topics such as file operations, database connection, digital image processing, and artificial intelligence will be discussed, particularly within the field of health informatics.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Fall, Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4611 or CS 5611

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): CS 4821

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 or CS 4411

Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in even years
• Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI topics such as risk prediction, medical image analysis, natural language processing of clinical text, computer vision, and the integration of AI, bias in algorithm development, bioethics, and regulation into the clinical environment are covered.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Set in a Linux environment, students will learn to design computational workflows, translate problems into programs, understand sources of errors, and debug, profile and parallelize the code. Successful completion of FOSS101 and earning its Digital Badge are required prior to registration

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

Introduces the general concepts and algorithms of machine learning (ML) with their implementation and applications to practical problems of modeling, detection, estimation, prediction, and control. Applications include cybersecurity, healthcare, robot vision, remote sensing, automation, and natural language processing.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
• Pre-Requisite(s): SAT 4310 or SAT 4650 or CS 1121

Course covers fundamental subjects such as medical decision support systems, telemedicine, medical ethics and biostatistics. Topics include consumer health informatics, international health care systems, global health informatics, and translational research informatics. Students will see medical informatics from a diverse scope of healthcare industry organizations. Scientific writing and communication will be encouraged.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. Topics include improving physical order entry and healthcare, using medical literature, clinical case discussions, meaningful use of medical data, enhancing patient and care-giver education, disease prevention, and public health and environmental health informatics.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 5114

Course will cover concepts and techniques used to analyze big data. We will cover the most important big data processing frameworks (e.g. Hadoop, spark) and GPU techniques. The students will acquire the knowledge of Hadoop architecture, MapReduce, Spark and the capability of programming to analyze big data.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 4650

Course will consist of the legal and regulatory requirements and security privacy concept principles regarding data management. Best practices of how organizations manage information risk through risk assessment practices and procedures will be conducted.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following College(s): College of Computing, College of Engineering, College of Business

This course explores the foundations of population health informatics, including information architecture, data standards and confidentiality. We will examine key concepts related to registries, electronic health records, epidemiological databases, biosurveillance, health promotion, and quality reporting in population health management.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): SAT 4650

Study of artificial intelligence and machine learning in cybersecurity. Topics include fundamentals of common machine learning and deep learning algorithms, intelligent threat detection and analysis, user behavior analytics, machine learning in hacking, and automated cybersecurity systems.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): SAT 3812 and SAT 4310

High-level review of geospatial data acquisition systems, sensors and associated processing technologies. Course considers geospatial metadata generation principles, interoperability, and major tools for manipulation with geospatial data. Course may help in transition of non-geospatial majors to geospatial field.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Major(s): Integrated Geospatial Tech, Surveying Engineering, Geospatial Engineering

This course teaches how to quantify data out of images, typically from optical microscopes. It covers thresholding, image derivatives, edge-detection, watershed, multi-scale and steerable filters, 3D image processing, feature extraction, PCA, classification, convolutional neural networks, particle tracking, and diffusion analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-1-2)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Study of financial statement analysis and concepts of valuation utilizing accounting based financial information. Methods are applied to encompass decision making, communication, and judgment using problems, cases, and projects.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Accounting; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

This class covers the collection, reporting, and analysis of financial information with emphasis on the use of that information to support decision making.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Applied Natural Resource Econ., Engineering Management, Accounting, Business Administration

Applications and case studies focusing on contemporary issues in operations and quality management to include lean manufacturing practices, ERP, quality and environmental management systems/standards, Six Sigma, statistical process control, and other current topics.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration

Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100

The course facilitates students' improvement of analytical skills, information processing techniques, and cultural competence in the globalized marketing environment. Focuses are placed on strategic marketing management, high-tech product marketing, global consumer behavior, branding, and online marketing.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration

Introduces techniques and procedures to estimate and test economic and financial relationships developed in business, economics, social and physical sciences.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (EC 2001 or EC 3002 or EC 3003) and (BUS 2100 or MA 2710 or MA 2720 or MA 3710) and (MA 1135 or MA 1160 or MA 1161 or MA 1121)

Analysis of asset and liability management of financial institutions and the role of financial institutions in the U.S. and international economy.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (EC 3003 or FIN 3000) and UN 1015 and (UN 1025 or Modern Language - 3000 level or higher)

Introduction to the principles of finance. Topics include financial mathematics, the capital investment decision, financial assets valuation, and the risk-return relationship

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring, Summer
• Pre-Requisite(s): ACC 2000 and (MA 1020 or MA 1030 or MA 1031 or MA 1032 or MA 1120 or MA 1135 or MA 1160 or MA 1161 or MA 1121 or MA 2160 or ALEKS Math Placement >= 61 or CEEB Calculus AB >= 2 or CEEB Calculus BC >= 2 or ACT Mathematics >= 22 or SAT MATH SECTION SCORE-M16 >= 540)

Covers the pricing and use of options, financial futures, swaps, and other derivative securities.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): EC 3400 or FIN 3000 and (MA 2710 or MA 2720 or MA 3710)

Develops an entrepreneurial mindset and a personal toolkit of methods and practices that enables students to create and evaluate entrepreneurial opportunities, marshal resources, and engage in entrepreneurial teams driven by creativity, leadership, smart action, and innovation.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman

Provides an understanding of the IS development and modification process and the evaluation choices of a system development methodology. Emphasizes effective communication with users and team members and others associated with the development and maintenance of the information system. Stresses analysis and logical design of departmental-level information system.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MIS 2000(C) or MIS 2100(C) or CS 1122 or CS 1131

Focuses on understanding IT for competitive advantage and as an agent of transformation. Topics include managing IT infrastructure and architecture, facilitating information distribution throughout the enterprise, business applications for machine learning and artificial intelligence, and other emerging trends and technologies.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (MIS 2100 and MIS 3200(C)) or (CS 2321 and CS 3141)

Focuses on generation and interpretation of business analytics relative to organizational decision making. Includes core skills necessary for constructing data retrieval queries in a relational database environment and processing data using appropriate programming languages. Introduces concepts related to data pipelining.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MIS 2100 or CS 1122 or CS 1131) and (MIS 3100 or CS 3425)

Examines current IS/IT topics and issues in greater depth from a managerial perspective. A single offering of this course will concentrate on one or two topics, which will vary.

• Credits: 3.0; Repeatable to a Max of 6
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): MIS 2000 or MIS 2100 or CS 1122 or CS 1131

Introduces students to models, theories, practices, and sociocultural issues pertinent to consumers' decision making and lifestyle choices. Discussions will be based on a variety of disciplines: psychology, sociology, economics, and anthropology.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MKT 3000

Focuses on data-driven consumer insights for marketing decision-making. Topics include scientific research methodology, survey research, social media data-analysis, multivariate data analysis, information visualization, and report writing and presentations.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or BUS 2100) and MKT 3000

Introductory study of the properties of polymers. Includes structure and characterization of polymers in the solid state, in solution, and as melts. Topics include viscoelasticity, rubbery elasticity, rheology and polymer processing. Applications discussed include coatings, adhesives, and composites.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Pre-Requisite(s): CH 1122 or (CH 1160 and CH 1161)

Advanced study of mechanistic organic and physical organic chemistry intended to bring the student to the level of current research activity. Topics may include methods for determining organic reaction mechanisms, chemical bonding as it applies to organic compounds, structure-reactivity relationships, molecular rearrangements, and molecular orbital theory.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Advanced study of organic reactions and synthetic organic chemistry intended to bring the student to the level of current research activity. Topics may include retrosynthetic analysis and synthesis design, synthons, protecting groups, and analysis of syntheses from recent literature.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Assessment of factors controlling environmental fate, distribution, and transformation of organic pollutants. Thermodynamics, equilibrium, and kinetic relationships are used to quantify organic pollutant partitioning and transformations in air, water, and sediments. Use of mass balance equations to quantify pollutant transport.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in odd years
• Pre-Requisite(s): CEE 4501 or CH 3510

Study of the photochemical processes governing the composition of the troposphere and stratosphere, with application to air pollution and climate change. Covers radical chain reaction cycles, heterogeneous chemistry, atmospheric radiative transfer, and measurement techniques for atmospheric gases.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CH 3510 or ENVE 4501 or ENVE 4504 or CEE 4501 or CEE 4504

This course is focused on the chemistry of atmospheric aerosols and cloud processes. Students will learn about methods for chemical characterization, the chemical composition of aerosol and the chemical reactions pertinent to secondary aerosol and cloud composition.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring, in odd years
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Focuses on the theory and method of modern computational techniques applied to the study of molecular properties and reactivity through lecture and computer projects. Covers classical mechanical as well as quantum mechanical approaches.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Pre-Requisite(s): CH 3520

Course covers multivariate statistics such as ANCOVA, Multiple Regression, factor analysis, clustering, machine learning, and mixture modeling.

• Credits: 3.0; Repeatable to a Max of 12
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Spring, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): PSY 5110

This course covers the design issues concerning the implementation of database management systems, including distributed databases. The topics include data storage, index implementation, query processing and optimization, security, concurrency control, transaction processing, and recovery.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): CS 3425

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): CS 3411 or CS 4411

Fundamental ideas and techniques that are used in the construction of problem solvers that use Artificial Intelligence technology. Topics include knowledge representation and reasoning, problem solving, heuristics, search heuristics, inference mechanisms, and machine learning.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
• Pre-Requisite(s): CS 2311 and CS 2321 and (CS 3411 or CS 3421 or CS 3425 or CS 3331) and MA 3720

Design and analysis of advanced algorithms. Topics include algorithms for complex data structures, probabilistic analysis, amortized analysis, approximation algorithms, and NP-completeness. Design and analysis of algorithms for string-matching and computational geometry are also covered.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4321

Advanced topics in the design, analysis, and performance evaluation of parallel algorithms. Topics include advanced techniques for algorithm analysis, memory models, run time systems, parallel architectures, and program design, particularly emphasizing the interactions of these factors.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): CS 4431 and CS 4331

Covers time and order in distributed systems; mutual exclusion, agreement, elections, and atomic transactions; Distributed File Systems, Distributed Shared Memory, Distributed System Security; and issues in programming distributed systems. Uses selected case studies.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): CS 4411 and CS 4461

Current issues in human-computer interaction (HCI), evaluation of user interface (UI) design, and usability testing of UI. Course requires documenting UI design evaluation, UI testing, and writing and presenting a HCI survey, concept or topic paper.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): CS 4760

Course topics include current topics in artificial intelligence including agent-based systems, learning, planning, use of uncertainty in problem solving, reasoning, and belief systems.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): CS 4811

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall
• Restrictions: Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering, Electrical & Computer Engineer; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Detecting and estimating signals in the presence of noise. Optimal receiver design. Applications in communications, signal processing, and radar.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, Spring
• Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
• Pre-Requisite(s): EE 5500

Building blocks of wireless sensor networks, sensor node design, wireless communications, network protocols, data storage and retrieval, sensor localization and clock synchronization. Example application areas: robotics, autonomous vehicles and networks, power engineering, smart-grid, environment monitoring, and disaster relief.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Pre-Requisite(s): (CS 4461 or EE 4272 or EE 5722) and (EE 3170 or EE 3173) and (CS 1129 or CS 2141)

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: On Demand
• Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

This course is designed for graduate students majoring in forestry, wildlife, ecology, and natural resource management and data science to develop fundamental but essential skills for data presentation and visualization through generating informative graphs with R.

• Credits: 2.0
• Lec-Rec-Lab: (1-0-2)
• Semesters Offered: Spring, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Using statistical tools to analyze data from ecology, forestry and environmental science. Topics include multiple linear, curvilinear and non-linear regression, hierarchical grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data using R.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Spring, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5412

Use of R for basic data manipulation, statistical summary and statistical analysis. Topics include installing R, data import, handling and manipulation, basic statistics, graphical outputs and fitting of linear, non-linear and mixed-effects models.

• Credits: 1.0
• Lec-Rec-Lab: (0-1-0)
• Semesters Offered: Spring, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Co-Requisite(s): FW 5411

Remote sensing principles and concepts. Topics include camera and digital sensor arrays, types of imagery, digital data structures, spectral reflectance curves, applications, and introductory digital image processing.

• Credits: 3.0
• Lec-Rec-Lab: (2-1-0)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Co-Requisite(s): FW 5541

Use of geographic information systems (GIS) in resource management. Studies various components of GIS in detail, as well as costs and benefits. Laboratory exercises use ArcGIS software package to solve resource management problems.

• Credits: 4.0
• Lec-Rec-Lab: (3-0-3)
• Semesters Offered: Fall
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710

This course moves beyond the fundamentals of GIS to explore the application of GIS technology to environmental monitoring and resource management issues. Students learn graphic modeling techniques, network analysis, 3D visualization, geodatabase construction and management, and multivariate spatial analysis.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): FW 5550

Course provides exposure to data collection techniques, web mapping applications, and advanced database structures. Students will investigate GIS system design, GIS project planning and data management, learn map atlas creation and cartographic techniques, and discuss geospatial ethics.

• Credits: 3.0
• Lec-Rec-Lab: (1-0-4)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
• Pre-Requisite(s): FW 5550

Presents the theory and quantitative procedures of digital image processing using remotely sensed data. Emphasizes image acquisition, preprocessing, enhancement, transformation classification techniques, accuracy assessment, and out-products. Discusses linkages to GIS. Also covers evaluating applications of the technology to current resource management problems via peer-reviewed literature.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Spring, in odd years
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Exploration of how to develop comprehensive plans to mitigate the impact of natural hazards on humans. Requires a project and report.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Will prepare students, including those with no seismology background, to interpret seismic and acoustic signals from volcanoes. Topics: basic seismology, monitoring techniques, tectonic and volcanic earthquakes, infrasound, deformation over a range of time scales.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Spring, in even years
• Pre-Requisite(s): (MA 1160 or MA 1161 or MA 1121 or MA 1135) and GE 2000 and PH 2100

This course covers the concepts and theories in geospatial science, GIS analysis techniques (network analysis, cost distance analysis, multi-layer raster data analysis), and remote sensing theories and applications across different spectra. Basic concepts and techniques associated with geostatistics, and analysis of spatial relationships are also introduced using examples in geophysical and environmental research.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-2)
• Semesters Offered: Spring
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

Principles and application of reflection seismic techniques. Includes acquisition, data processing, and 2D/3D data interpretation. Project and report required.

• Credits: 3.0
• Lec-Rec-Lab: (2-1-0)
• Semesters Offered: On Demand
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

This course covers the handling of spatial and temporal data for knowledge discovery. Major topics include spatial interpolation, clustering, association analysis, and supervised and unsupervised classification. Students will learn how to use geostatistical and pattern recognition tools for geoscience applications.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-1)
• Semesters Offered: Fall, Spring

A study of fundamental ideas in linear algebra and its applications. Includes review of basic operations, block computations; eigensystems of normal matrices; canonical forms and factorizations; singular value decompositions, pseudo inverses, least-square applications; matrix exponentials and linear systems of ODEs; quadratic forms, extremal properties, and bilinear forms.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (MA 2320 or MA 2321 or MA 2330) and MA 3160

Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The SAS statistical package is an integral part of the course.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, Summer
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

Basic algorithmic and computational methods used in the solution of fundamental combinatorial problems. Topics may include but are not limited to backtracking, hill-climbing, combinatorial optimization, linear and integer programming, and network analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in even years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Review of basic graph theory followed by one or more advanced topics which may include topological graph theory, algebraic graph theory, graph decomposition or graph coloring.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in odd years
• Restrictions: Must be enrolled in one of the following Level(s): Graduate
• Pre-Requisite(s): MA 5301 or MA 4209

Design and analysis of algorithms for problems in linear algebra. Covers floating point arithmetic, condition numbers, error analysis, solution of linear systems (direct and iterative methods), eigenvalue problems, least squares, and singular value decomposition. Includes the use of appropriate software including high performance computational libraries.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 4330 or MA 4630

Numerical solution of unconstrained and constrained optimization problems and nonlinear equations. Topics include optimality conditions, local convergence of Newton and Quasi-Newton methods, line search and trust region globalization techniques, quadratic penalty and augmented Lagrangian methods for equality-constrained problems, logarithmic barrier method for inequality-constrained problems, and Sequential Quadratic Programming.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in even years
• Pre-Requisite(s): MA 4330 or MA 4610 or MA 4630 or MA 5627

Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320

Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in odd years

Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)

Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in odd years

Course introduces graduate students to conventions of professional engineering communication such as composing technical documents and working effectively in teams. Students will practice creating effective visuals for reports and slides and develop and deliver presentations.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Pre-Requisite(s): MEEM 4901(C) or ENT 4950(C) or Graduate Status >= 1

An overview of numerical and computer methods to analyze and visualize physics problems in mechanics, electromagnetism, and quantum mechanics. Utility and potential pitfalls of these methods, basic concepts of programming, UNIX computing environment, system libraries and computer graphics are included.

• Credits: 3.0
• Lec-Rec-Lab: (2-0-3)
• Semesters Offered: Fall
• Pre-Requisite(s): PH 2020 and PH 3410

An introduction to computational methods for the social sciences. The course provides an introduction to complexity theory and Agent-Based Modeling. Students will apply what they have learned in this course to develop a pilot simulation to understand any social phenomena of their choosing.

• Credits: 3.0
• Lec-Rec-Lab: (3-0-0)
• Semesters Offered: Fall, in even years
• Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.

• Credits: variable to 2.0; May be repeated
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate