Courses
| Course Title | Credit Hours | Description |
|---|---|---|
| ANTH 449 / 660 Big-Data Social Sciences | (3) | Scheduled topics include data-mining and text, sentiment analysis, spatial mapping of social data, multivariate or multidimensional analysis, social networks, social diffusion. The class will focus on manipulating and visualizing data from public sources. Students will consider aspects of data publication and understanding digital scholarship. Uses R Studio. Formerly ANTH 511. |
| ANTH 504 Anthropological Statistics I | (3) | Introduction to frequentist statistics (including standard parametric statistics and some of their nonparametric correlates) emphasizing anthropological applications, available methods and appropriate usage. Exploratory data sets will be provided to facilitate learning, and students may additionally provide their own data. |
| ANTH 604 Anthropological Statistics II | (3) | Advanced frequentist statistics for anthropology focusing on multivariate methods, time series analyses, resampling statistics, maximum likelihood analyses, with an introduction to Bayesian approaches. While exploratory data sets will be provided, students are strongly encouraged to supply their own research data to facilitate learning. Registration Restriction(s): Must have completed Anthropology 504 or its equivalent with at least a B. Minimum student level – graduate. |
| Course Title | Credit Hours | Description |
|---|---|---|
| BCMB 401 Biochemistry I | (4) | First semester of a two-course sequence providing in-depth coverage of biochemistry and molecular biology. Covers amino acid structure and chemistry, protein structure and chemistry, protein folding, enzyme behavior and function, reaction mechanisms, catabolism and energy transfer, synthetic metabolism including photosynthesis, and protein transport. (DE) Prerequisite(s): Biology 240 and Chemistry 350, 360, and 369. |
| BCMB 402 Biochemistry II | (4) | Second semester of a two-course sequence providing in-depth coverage of biochemistry and molecular biology. Covers structure of DNA and RNA, experimental methods of analyzing nucleic acids, mechanisms of RNA and protein synthesis, mechanisms of DNA replication, repair and recombination, chromosome structure and function, regulation of gene expression, genome structure and genomics, and mechanisms of biological regulation. (DE) Prerequisite(s): Biology 240 and Chemistry 350, 360, and 369. |
| BCMB 471 Biophysical Chemistry | (3) | Physicochemical principles with applications to biological systems. Thermodynamics; chemical equilibrium; solution chemistry; transport; electrochemistry; kinetics; enzyme catalyzed reactions. Cross-listed: (Same as Chemistry 471.) (DE) Prerequisite(s): Chemistry 350 and 360, Mathematics 125, and general biology or consent of instructor. |
| BCMB 481 Biophysical Chemistry | (3) | Physicochemical principles with applications to biological systems. Elementary quantum chemistry; interactions of light with biological molecules; optical and magnetic spectroscopy; light scattering; case studies of selected macromolecules. Cross-listed: (Same as Chemistry 481.) (DE) Prerequisite(s): Chemistry 350 and 360, Mathematics 125, and general biology or consent of instructor. |
| BCMB 510 Computational Structural Biochemistry | (1) | Introduction to computational tools, internet resources and databases for biological research to analyze and model protein structures and to study protein-ligand interactions. (DE) Corequisite(s): 511. Registration Permission: Consent of instructor. |
| BCMB 511 Advanced Protein Chemistry and Cellular Biology | (3) | Cellular structure and function at molecular and supramolecular level in progression: protein structure and function; membrane structure and function; bioenergetics and membrane proteins. (DE) Corequisite(s): 510. Recommended Background: Prior knowledge of cell biology and biochemistry. Registration Permission: Consent of instructor. |
| BCMB 512 Advanced Molecular Biology | (3) | Regulation of nucleic acid expression and protein activity. Nucleic acid structure and function; replication and repair of nucleic acids; gene expression; protein synthesis; post-translational protein modification; mitosis and meiosis; cell cycle and cell growth. (DE) Prerequisite(s): 511 or consent of instructor. |
| BCMB 515 Experimental Techniques I | (2-4) | Introduction to modern experimental methodology and instrumentation in biochemistry, molecular biology and cell biology, including cell culture; spectrophotometry; microscopy; nucleic acid purification and analysis; protein assays; enzyme purification; electrophysiology; computer analysis of nucleic acid and protein sequences. Team-taught lecture/demonstration format. Repeatability: May be repeated. Maximum 6 hours. Comment(s): Primarily for departmental graduate students. |
| BCMB 517 Physical Biochemistry | (3) | Physics and chemistry of biological systems and molecules. Thermodynamics; diffusion and transport; physical chemistry of macromolecules; enzyme kinetics; binding reactions; spectroscopy; electrophysiology. (DE) Prerequisite(s): 511 or consent of instructor. |
| BCMB 560 Advanced Concepts in Structural Biology/Biochemistry | (3) | Concepts related to structural biology/biochemistry with information taken from current literature. Predominantly lecture format with student participation. Specific subject area to be announced. Repeatability: May be repeated. Maximum 12 hours. Registration Permission: Consent of instructor. |
| BCMB 615 Special Topics in Biochemistry, Cellular, and Molecular Biology | (3) | Biochemical and biophysical methods, mechanisms of enzyme catalysis, gene expression, membrane structure and function, metabolic regulation, physical biochemistry, molecular genetics, cell ultrastructure and physiology, neurobiology, and related topics. Repeatability: May be repeated. Maximum 9 hours. (DE) Prerequisite(s): 511 and 512 or consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| CBE 506 Advanced Engineering Mathematics | (3) | Formulation and solution of problems in chemical engineering and materials areas, ordinary and partial differential equations; types of ODE, PDE and solution techniques; transform methods; conformal mapping; variational methods; introduction to numerical methods. Cross-listed: (Formerly CBE 505. Same as MSE 506/510.) |
| CBE 547 Advanced Transport Phenomena | (3) | Unified treatment of momentum transport (fluid flow), energy transport (heat conduction, convection, and radiation), and mass transport (diffusion). Fundamental basis of transport phenomena and momentum transport: viscous, viscoelastic, and potential flows. (Formerly CBE 548) |
| CBE 633 Multiscale Materials Modeling | (3) | Development of multiscale simulation strategies for engineering of advanced micro-and-nano structured materials via integration of essential information from different scales, i.e., molecular, mesoscopic and continuum. (Also taught as Special Topics CBE 691 in spring 2012.) |
| Course Title | Credit Hours | Description |
|---|---|---|
| CE 538 / 561 Finite Element Applications for Geotechnical/Structural Engineering | (3) | Application of finite element method to typical problems in geotechnical engineering. Confined and unconfined flow through porous media; two-dimensional stress and strain; two-dimensional elements; representation of nonlinear soil behavior with elastic and elastic-plastic models. Taught concurrently with 561. Application of finite element method to typical problems in structural engineering. Truss, beam and plate elements; two-dimensional stress and strain; two-dimensional elements; representation of nonlinear material behavior with elastic and elastic-plastic models. Taught concurrently with 538. Credit Restriction: Students may not receive credit for both 538 and 561. Recommended Background: Course work in soil behavior and matrix computation. |
| CE 551 Traffic and Engineering Characteristics | (3) | Characteristics of human, vehicle, and roadway in transportation system; microscopic and macroscopic traffic models; elements of transportation/highway safety. |
| ENVE 561 Climate and Environmental Informatics | (3) | Introduction to applied time series, spatial statistics, and geographical data sciences for climate and the environmental applications with an emphasis on extreme events, regional analysis, uncertainty characterization and risk management. Case studies and class projects focused on integration of disparate data and analysis techniques to solve problems in climate change impacts. |
| ENVE 562 Three Dimensional Climate Modeling | (3) | Theory and applied algorithms for three-dimensional climate modeling including conservation laws, prognostic and diagnostic relationships and climate model formulations. emphasis on numerical methods, coordinate systems, spatial and temporal discretizations, parameterization and model validation. |
| ENVE 577 Air Pollution Climatology | (3) | Linkages between climate change and pollutant emissions, transport, transformation, and deposition. Both the impact of air quality on climate and the impact of climate on air quality will be examined using general circulation and meteorological models. Regional-scale effects of land utilization, incident radiation, climate perturbations and air quality parameters such as ozone, particulate matter, and greenhouse gases will be investigated. |
| ENVE 595 Water Resources and Atmospheric Impacts of Climate Change | (3) | Problems and topics related to current developments in field. |
| ENVE 595 / 691 Special Topic: Impact Analysis of the Climate Extreme Events | (3) | Research outcomes. Theory and practice of extreme events in climate change issues. The course will emphasize new development of climate studies. Students will learn critique extreme events articles and publications. The earth system models, GCMs, will be introduced and how to use its outputs to assess climate extreme effects. Global and regional climate modeling for impacts of extreme events based on IPCC CMIPS data availability and analysis of impacts of the climate extreme in large scale statistical and mathematical data analysis. The tools will be used such as empirical code, Data Mining, NCL, IDL, and Matlab. The Kraken and Newton machines are available for the data simulations and analyses. Hopefully students will have some accomplishment through this course, such as a draft for a conference and/or journal paper and useful findings. |
| CE 595 Software Development for Research & Practice | (3) | Graduate students in engineering often face a significant challenge to develop software at an advanced level to support their thesis research or to prepare for a career in using/developing/supporting software for engineering practice/research. This course provides a much-needed bridge from the typical undergraduate use of computing in engineering (e.g. spreadsheet macros, simple Matlab and Mathematica scripts) to current approaches for development of modern research and production-scale software. Engineering students completing this course are also prepared to study parallel programming, graphics/visualization, databases, algorithms, etc. which assume a working knowledge and experience with abstract programming concepts in C++ and Python. Students in this course acquire specific knowledge and skills with object-oriented techniques in C++ sufficient to design, implement and test moderate-size programs for engineering computations. Students develop knowledge of modern computer hardware, parallel processing and operating systems (Linux, Windows, OS X) at a level to understand the impact and interaction with engineering application software. Credit restriction: IGMCS credit is limited to CE595: Software Development for Research & Practice. Other CE595 courses are not eligible unless stated otherwise. |
| CE 651 Analysis Techniques for Transportation Systems I | (3) | Topics on mathematical, statistical, operations research, or computer science techniques that may be applied to modeling and analysis of transportation systems. Registration Restriction(s): Minimum student level - graduate. Registration Permission: Consent of instructor. |
| CE 652 Analysis Techniques for Transportation Systems II | (3) | Advanced topics of application of mathematical, statistical and computer science techniques in modeling and analysis of transportation systems. (DE) Prerequisite(s): 651. Registration Restriction(s): Minimum student level - graduate. |
| CE 661 Nonlinear Finite Element Methods | (3) | Theory and practice of nonlinear finite element methods focused on solid mechanics applications. Treatment of geometric nonlinear kinematics and inelastic material response. Development of linearized expressions for use in the Newton-Raphson algorithm. Programming of element and material subroutines. Cross-listed: (Same as: Mechanical Engineering 660 and Aerospace Engineering 660.) (DE) Prerequisite(s): CE 560 or equivalent; CE 538 or CE 561, or consent of instructor. |
| ENVE 672 Air Pollution Dispersion Modeling | (3) | Diffusion of air pollution in the atmosphere; application of USEPA computer models for atmospheric dispersion from industrial, area, mobile sources, and spills; evaluation and production of meteorological data and comparison of model predictions to ambient measurements; new source review and permitting requirements. Models have been in parallel computation. (DE) Prerequisite(s): 574. |
| ENVE 691 Advanced Climate and Air Pollution Modeling | (3) | Selected advanced problems of current interest. |
| Course Title | Credit Hours | Description |
|---|---|---|
| CHEM 570 Quantum Chemistry and Spectroscopy | (3) | Basic principles of quantum mechanics and their applications to molecular orbital theory, molecular structure, and spectroscopy; introduction to group theory. Recommended Background: 2 semesters of physical chemistry. |
| CHEM 572 Thermodynamics and Statistical Mechanics | (3) | Macroscopic and microscopic description of equilibrium systems. Basic principles of thermodynamics and statistical mechanics, and application to selected chemical systems. Recommended Background: 2 semesters of physical chemistry. |
| CHEM 670 Special Topics in Physical Chemistry | (3) | Topics of current significance. At present, Coupled-Channel Methods for Quantum Dynamics is the only 670 topic approved. However, other topics may qualify. See the Chemistry Dept. liaison for more information. Repeatability: May be repeated. Maximum 12 hours. (DE) Prerequisite(s): 570, 572, and 573 or consent of instructor. Section offered in Fall of 2011 is not IGMCS eligible. |
| Course Title | Credit Hours | Description |
|---|---|---|
| COSC 425 Machine Learning | (3) | Machine learning is concerned with computer programs that automatically improve their performance through experience. Covers the theory and practice of machine learning from a variety of perspectives. We cover topics such as learning decision trees, neural network learning, statistical learning methods, genetic algorithms, Bayesian learning methods, explanation-based learning, and reinforcement learning. Programming assignments include hands-on experiments with various learning algorithms. (RE) Prerequisite(s): 302; Electrical and Computer Engineering 313 or Mathematics 323. Comment(s): Prior knowledge may satisfy prerequisite with consent of instructor. |
| COSC 505 Programming for Scientists and Engineers | (3) | This course will provide an introduction to high-performance scientific computing tools, methods, and environments for the solution of problems in science and engineering. This course is NOT for EECS students. Formerly COSC 594 Programming for Scientists and Engineers. |
| COSC 522 / 528 Introduction to Machine Learning | (3) | Theoretical and practical aspects of machine learning techniques that enable computer systems to learn from experience. Methods studied include concept learning, decision tree learning, neural networks, Bayesian learning, instance-based learning, genetic algorithms, rule learning, analytical learning, and reinforcement learning. Recommended Background: 302, 311, Mathematics 251; and Mathematics 323, or Electrical and Computer Engineering 313. This course may not be approachable to non-COSC majors. Note: The above course is available online for Fall 2020. |
| COSC 525 Deep Learning | (3) | Theoretical and practical aspects of how to build deep networks for representations of high-dimensional data. Deep models for both supervised and unsupervised learning will be discussed, including convolutional neural network, autoencoder, generative adversarial network, and recurrent neural network. Recommended Background: Machine Learning and Python Programming Language. |
| COSC 526 Data Mining | (3) | A comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling, association analysis, clustering, classification, and anomaly detection. Prereq: Discrete mathematics or statistics and programming. |
| COSC 527 Biologically Inspired Computation | (3) | IGMCS IGMCS 100% 11 C7 A course that explores information processing and self-organization in biological systems. Topics include dynamical systems concepts (attractors, basins of attraction, Wolfram classes, stability, Lyapunov functions, information theory, thermodynamic limits of computation), cellular automata (Langton's lambda, phase transitions, computation and life at the "edge of chaos"), and excitable media (cardiac tissue, slime mold, reaction-diffusion systems, activator-inhibitor systems, Turing patterns and animal hair-coats), just to name a few. Students' understanding of complex systems and dynamical processes is enhanced by videos of biological systems and in-class demonstrations and experiments using multi-agent simulations. Prerequisites: basic programming ability, linear algebra (e.g., Math 251), differential equations (e.g., Math 231, 241, but primarily just the concepts of differential equations and partial derivatives), probability and statistics (e.g., Math 323). Basic biology and physics are helpful as well. A course that explores information processing and self-organization in biological systems. Topics include dynamical systems concepts (attractors, basins of attraction, Wolfram classes, stability, Lyapunov functions, information theory, thermodynamic limits of computation), cellular automata (Langton's lambda, phase transitions, computation and life at the "edge of chaos"), and excitable media (cardiac tissue, slime mold, reaction-diffusion systems, activator-inhibitor systems, Turing patterns and animal hair-coats), just to name a few. Students' understanding of complex systems and dynamical processes is enhanced by videos of biological systems and in-class demonstrations and experiments using multi-agent simulations. Prerequisites: basic programming ability, linear algebra (e.g., Math 251), differential equations (e.g., Math 231, 241, but primarily just the concepts of differential equations and partial derivatives), probability and statistics (e.g., Math 323). Basic biology and physics are helpful as well. |
| COSC 530 Computer Systems Organization | (3) | Architectures and systems organization for serial and parallel machines. Recommended Background: Course work in architecture or machine organization. Note: the above course is available online for Fall 2020. |
| COSC 557 Computer Architecture and Design | (3) | An exploration of the central issues in computer architecture: instruction set design, addressing and register set design, control unit design, microprogramming, memory hierarchies (cache and main memories, mass storage, virtual memory), pipelining, bus organization, RISC (Reduced Instruction Set Computers), and CISC (Complex Instruction Set Computers), implementation issues, technology trends, architecture modeling and simulation. |
| COSC 557 Visualization | (3) | IGMCS IGMCS 100% 11 C10 Graphical techniques to reveal intrinsic properties in data, acquired or computationally simulated, from various scientific, medical and engineering applications. Topics may include visual perception, structure and storage of high-dimensional data (structured and unstructured), visualization of scalar fields, vector fields, tensor fields, or other complex quantities, time-varying data, advanced light transport (single-scattering and multiple scattering), transfer functions, graphs and manifolds, level sets, interpolation, hierarchical and paralle acceleration methods. The design and use of leading production visualization packages will also be covered. Recommended background: COSC 556 Computer Graphics. Graphical techniques to reveal intrinsic properties in data, acquired or computationally simulated, from various scientific, medical and engineering applications. Topics may include visual perception, structure and storage of high-dimensional data (structured and unstructured), visualization of scalar fields, vector fields, tensor fields, or other complex quantities, time-varying data, advanced light transport (single-scattering and multiple scattering), transfer functions, graphs and manifolds, level sets, interpolation, hierarchical and paralle acceleration methods. The design and use of leading production visualization packages will also be covered. Recommended background: COSC 556 Computer Graphics. |
| COSC 565 Databases and Scripting Languages | (3) | Introduction to database theory, models, and query formation. Survey of scripting languages, their uses, and their interconnectivity with databases. Recommended Background: COSC 302. |
| COSC 571 Pattern Recognition | (3) | Decision-theoretic and structural approaches to pattern recognition. Deterministic and statistical decision rules, feature extraction and representation, syntactic and semantic methods. Recommended Background: Coursework in probability or statistics. |
| COSC 571 Numerical Mathematics I | (3) | See Math 571. |
| COSC 594 Introduction to Computer Science for Computational Scientists | (3) | An introduction to high performance computing, data structures, parallel processing techniques, building a cluster, performance issues, design of algorithms, use of sw packages. Emphasis on program design, data structures, computational complexity, and scientific computing environments. Prerequisites: programming and numerical methods. |
| COSC 594 Computer Systems Organization | (3) | Architectures and systems organization for serial and parallel machines. Recommended Background: Course work in architecture or machine organization. (also listed as CS 530) |
| COSC 594 Scientific Computing for Engineers | (3) | Spring 2012. Part I will start with current trends in high-end computing systems and environments, and continue with a practical short description on parallel programming with MPI, OpenMP, and Pthreads. Part II will illustrate the modeling of problems from physics and engineering in terms of partial differential equations (PDEs), and their numerical discretization using finite difference, finite element, and spectral approximation. Part III will be on solvers: both iterative for the solution of sparse problems of part II, and direct for dense matrix problems. Algorithmic and practical implementation aspects will be covered. Finally in Part IV, various software tools will be surveyed and used. This will include PETSc, Sca/LAPACK, MATLAB, and some tools and techniques for scientific debugging and performance analysis. |
| COSC 594 Programming for Scientists and Engineers | (3) | See COSC 505. |
| COSC 594 Graph Algorithms, Applications, and Implementations | (3) | This class is intended for graduate students who would like to learn about graph algorithms, applications and implementations. Lectures will be largely devoted to guided discussions. Participants will be expected to complete homework and reading assignments. Three hours credit will be given. Students in any academic discipline are welcomed. A basic interest in practical issues in computer and data science is expected. Familiarity with one or more programming languages will be needed. Prior background knowledge in graph theory is encouraged but not required. |
| Course Title | Credit Hours | Description |
|---|---|---|
| DSE 511 Introduction to Data Science and Computing I | (3) | Topics include: version control, scripting languages, relational and non-relational databases, proper use of data structures, introduction to data science work flows, introduction to project management, and applications. Registration Restriction(s): Minimum student level – graduate. |
| DSE 512 Introduction to Data Science and Computing II | (3) | Topics include: platforms for scalable computing including Map Reduce, Hadoop, Spark, and HPC, setting up computing in cloud, and modern data science work flows. Registration Restriction(s): Minimum student level – graduate. |
| DSE 537 Introduction to Data Analysis and Data Mining | (3) | Topics include: data visualization, data summaries, missing data, study design, communicating results, linear regression, ANOVA, decision trees, random forests, support vector machines, model diagnostics, cross validation, bootstrap, reproducible research skills. Hands on projects. Registration Restriction(s): Minimum student level – graduate. |
| Course Title | Credit Hours | Description |
|---|---|---|
| EEB 406 Models in Biology (Sec. 8) | (3) | Difference and differential equation models of biological systems. Cross-listed: (Same as Mathematics 405.) (Formerly EEB 461.) (DE) Prerequisite(s): Math 142, 148, 152, or permission from instructor. |
| EEB 511 Core Evolution | (4) | Readings, lectures, and discussion about key concepts in evolution. |
| EEB 560 Biometry | (3) | Statistical applications in biological research. Recommended: Statistics course. |
| EEB 581 Mathematical Biology I | (3) | Deterministic and stochastic models of populations, communities, and ecosystems. Cross-listed: (Same as Mathematics 581.) (DE) Prerequisite(s): MATH 431, 453, or permission from instructor. |
| EEB 582 Mathematical Biology II | (3) | Continuation of 581. Cross-listed: (Same as Mathematics 582.) (DE) Prerequisite(s): 581 or permission from instructor. |
| EEB 585 Mathematical Evolutionary Theory | (3) | Population genetics and evolutionary ecology. (Same as Mathematics 583.) (DE) Prerequisite(s): MATH 431, 453, or permission from instructor. |
| EEB 610 Advanced Topics in Mathematical, Theoretical and Computational Ecology | (1-3) | Exposure and in-depth training in contemporary topics and approaches important to advanced research in mathematical, theoretical, and computational ecology. Consult departmental listing for offerings. Repeatability: May be repeated with consent of department. Maximum 9 hours. |
| EEB 681 Advanced Mathematical Biology I | (3) | Selected topics in theoretical and applied mathematical ecology: population, community, ecosystem ecology and applied topics such as demography, ecotoxicology, epidemiology, environmental change, and resource management. Cross-listed: (Same as Mathematics 681.) Repeatability: May be repeated. Maximum 6 hours. (DE) Prerequisite(s): 581, 582, or permission from instructor. |
| EEB 682 Advanced Mathematical Biology II | (3) | Continuation of 681 (Same as Mathematics 682.) Repeatability: May be repeated. Maximum 6 hours. (DE) Prerequisite(s): 681 or permission from instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| GEOL 425 Data Analysis for Geoscientists | (3) | Overview of sampling schemes, data analysis, and statistical methods as applicable to Earth sciences. Recommended Background: Introductory geology and introductory calculus, or consent of instructor. |
| GEOL 524 GIS for Geoscientists | (3) | Introduction to Geographic Information Systems (GIS), which are computer systems for input, storage, manipulation, and display of data georeferenced to the surface of the Earth or other planetary bodies. Participants will become familiar with a GIS software package, learn fundamental concepts of mapping and data manipulation, and will design and execute a GIS project in their own area of geologic interest. |
| GEOL 525 Data Analysis for Geoscientists | (3) | Overview of sampling schemes, data analysis, and statistical methods as applicable to earth sciences. Recommended Background: Introductory geology and introductory calculus. |
| GEOL 539 Geologic Applications of Remote Sensing | (3) | An introduction to the use of visible, infrared, microwave/radio, and nuclear remote sensing techniques in the geologic study of the Earth. Topics covered include mineral spectroscopy, light scattering models, instrumentation for remote sensing, calibration and atmospheric removal, multi- and hyperspectral image cube analysis, and ground-truthing techniques. Emphasis on working directly with remote sensing data to solve geologic problems. Contact Hour Distribution: 2 hours lecture and one 2-hour lab. Recommended Background: Mineralogy, calculus and physics or consent of instructor. |
| GEOL 590 Special Problems in Geology | (1-3) | Student- or instructor-initiated course offered at the convenience of the department, with focus on specialized topics in the geological sciences. Repeatability: May be repeated. Maximum 12 hours. Registration Permission: Consent of instructor. |
| GEOL 675 Seminar in Geophysics | (3) | Advanced treatment of selected topics in geophysics. Repeatability: May be repeated. Maximum 9 hours. Registration Permission: Consent of instructor. |
| GEOL 685 Seminar in Hydrogeology | (3) | Repeatability: May be repeated. Maximum 9 hours. Registration Permission: Consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| GEOG 411 Intermediate Geographic Information Science | (3) | Concepts and methods of spatial analysis and their application using geographic information systems software and techniques. Emphasizes both theoretical and applied aspects of GIS. Contact Hour Distribution: 2 hours lecture and 2 hours lab. (DE) Prerequisite(s): 310 or consent of instructor. |
| GEOG 414 Spatial Databases and Data Management | (3) | Types, sources, acquisition, and documentation of spatial data. Spatial database management methods and strategies for data sharing. Contact Hour Distribution: 2 hours lecture and 2 hours lab. (DE) Prerequisite(s): 411 or consent of instructor. |
| GEOG 510 Geographic Software Design | (3) | Algorithms for spatial analysis, software design, and program implementation in stand alone and distributed computing environments. Repeatability: May be repeated. Maximum 6 hours. Registration Permission: Consent of instructor. |
| GEOG 512 Environmental Modeling and Geospatial Analysis | (3) | Geographic applications in environmental modeling and geospatial analysis. Topics include but are not limited to: modeling concepts, frameworks and approaches; Geographic Information Science (GIS) for modeling; spatial decision support systems and environmental modeling; modeling human-environmental systems; and dynamic systems modeling. Repeatability: May be repeated with consent of instructor. Maximum 6 hours. (DE) Prerequisite(s): 411. |
| GEOG 515 Topics in Quantitative Geography | (4) | Multivariate analysis applied to spatial and temporal problems in geography; research problems utilizing appropriate computer programs; usefulness to geographic research of techniques developed by other disciplines. Contact Hour Distribution: 3 hours lecture and 2 hours lab per week. Repeatability: May be repeated with consent of instructor. Maximum 8 hours. Recommended Background: 415 or consent of instructor. |
| GEOG 517 Geographic Information Management and Processing | (3) | Concepts and methods in management of geographic information. Database design, manipulation, sampling and analysis. Registration Permission: Consent of instructor. |
| GEOG 611 Seminar in Geographic Information Science | (3) | Repeatability: May be repeated. Maximum 6 hours. (DE) Prerequisite(s): 517 and 518 or consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| LFSC 507 Bioinformatics and Computational Biology | (1-3) | Topics to be covered include the application of computing, modeling, data analysis, and information technology to fundamental problems in the life sciences. Repeatability: May be repeated. Maximum 12 hours. |
| LFSC 510 Special Topics in Life Sciences | (1-3) | Specializations in biotechnology; cellular, molecular, and developmental biology; environmental toxicology; ethology; plant, physiology and genetics; and physiology. Repeatability: May be repeated. Maximum 9 hours. |
| LFSC 517 Genomics and Bioinformatics | (3) | This course covers molecular evolution and comparative genomics. Computational approaches are a cornerstone of this field. It is taught by computational biologist Dr. Igor Zhulin. |
| LFSC 520 Genome Science and Technology I | (4) | Overview of genomics, advanced genetics principles. Either LFSC 520 or LFSC 521, but not both, may be counted toward the IGMCS. |
| LFSC 521 Genome Science and Technology II | (4) | Analytical technologies and special techniques. Either LFSC 520 or LFSC 521, but not both, may be counted toward the IGMCS. |
| LFSC 595 Special Topics in Genome Science and Technology | (1-3) | Tutorials or lectures in variety of special topics to be chosen by instructor. Eligible sections include: A Survey of Biology for Computational Researchers. Repeatability: May be repeated. Maximum 12 hours. |
| LFSC 596 Special Topics in Genome Science and Technology | (1-3) | Tutorials or lectures in variety of special topics to be chosen by instructor. Repeatability: May be repeated. Maximum 12 hours. |
| LFSC 695 Advanced Topics in Genome Science and Technology | (1-3) | Tutorials or lectures on variety of advanced topics to be chosen by instructor. Repeatability: May be repeated. Maximum 12 hours. |
| LFSC 696 Advanced Topics in Genome Science and Technology | (1-3) | Tutorials or lectures on variety of advanced topics to be chosen by instructor. Repeatability: May be repeated. Maximum 12 hours. |
| Course Title | Credit Hours | Description |
|---|---|---|
| INSC 565 Digital Libraries | (3) | Technological and social aspects of electronic publishing and digital libraries. Technologies and standards that enable electronic publishing and digital libraries. History of electronic publishing and digital libraries and their impact on user needs and information provision. Note: This course is also available online for distance education students. Contact the Information Sciences liaison for more information. Offered every spring semester. |
| INSC 584 Database Management Systems | (3) | Defining data needs, data structures, role of operating systems in data management, file organization, database management systems, logical data models, internal data models, database administration and evaluation. Design and implementation of application using database management system. Note: This course is also available online for distance education students. Contact the Information Sciences liaison for more information. Offered every fall semester. |
| INSC 588 Human-Computer Interaction | (3) | Survey of human-computer interaction and introduction to human and technological factors of importance to design of usable information systems. Basic phenomena of human perception, cognition, memory, and problem solving, and relationship to user-centered design. Methods and techniques for interaction design and evaluation. Note: This course is also available online for distance education students. Contact the Information Sciences liaison for more information. Offered every spring semester. |
| INSC 592 Introduction to Data Analytics and Visualization | (3) | Introduces the concepts of big data and data analytics in academics, businesses, sciences, the Web, etc. To master basic concepts and process of data analytics. To practice data mining techniques and skills (ETL). To design effective information visualizations. Pre-requisite: Database Management Systems (completion of IS584 or equivalent) Registration Permission: Consent of Instructor Required for the Students Outside of Information Sciences. Note: This course is also available online for distance education students. |
| INSC 594 Graduate Research Participation | (3) | Advanced research techniques under supervision of staff research director whose area coincides with interests of student. Grading Restriction: Satisfactory/No Credit grading only. Repeatability: May be repeated. Maximum 6 hours. Registration Permission: Consent of advisor and research director. Note: Credit towards IGMCS requires prior approval from the IGMCS liaison. Note: This course is also available online for distance education students. Contact the Information Sciences liaison for more information. |
| INSC 599 Practicum | (3-6) | Opportunity to translate theory into practice under guidance of qualified information professionals. Grading Restriction: Satisfactory/No Credit grading only. Repeatability: May be repeated. Maximum 6 hours. Recommended Background: Completion of core and pertinent advanced courses relevant to student's practicum design. Comment(s): 3.0 GPA required. Registration Permission: Consent of advisor and approval of practicum coordinator. Note: This course is also available online for distance education students. Contact the Information Sciences liaison for more information. |
| Course Title | Credit Hours | Description |
|---|---|---|
| IE 552 Advanced Linear Programming and Extensions | (3) | Linear programming solution procedures, duality, sensitivity, and parametric analysis; and quadratic, separable, integer, and goal programming. |
| IE 602 Nonlinear Optimization | (3) | Kuhn-Tucker theory in nonlinear programming, solution procedures for constrained and unconstrained nonlinear programs, search techniques, quadratic programming, duality and sensitivity analysis. Cross-listed: Management Science 651 |
| IE 604 Network Flow Optimization | (3) | Fundamental theory, algorithms, and applications of deterministic network-flow models, and analytical procedures for a special class of stochastic networks (GERT networks). Linear programming and its relationship to network analysis. Algorithms for various kinds of shortest and k-shortest path models. Labeling procedures for maximal flows in capacitated networks. The Out-Of-Kilter algorithm for cost minimization flow models. Primal Simplex network optimization procedures for pure and generalized networks. Traveling-Salesman problem algorithms with extensions to multiple salesmen. CPM, PERT and network-flow models in project management. Introduction to multi-commodity networks. Extensive use of network optimization software. (DE) Prerequisite(s): 522. Registration Restriction(s): Minimum student level – graduate. |
| IE 609 Stochastic Programming | (3) | Topics include modeling of uncertainty, two-stage stochastic programs, the value of information, Benders decomposition, L-shaped method, stochastic integer programs and multistage stochastic programs. |
| IE 610 Heuristics in Optimization | (3) | Heuristic methods and their applications to optimization problems, including neighborhood search and major meta-heuristics methods. |
| IE 611 Integer Programming | (3) | Theoretical foundations of Integer Programming and its application to optimization problems, including branch-and-bound, cutting planes, polyhedral analysis, and complexity. |
| Course Title | Credit Hours | Description |
|---|---|---|
| ME 504 Introduction to Uncertainty Quantification | (3) | Provides a foundational knowledge of uncertainty and propagation, quantification methodologies. It consists of 2 modules: I: Probability Concepts, Basic Statistical Operations, and Set Operations and II: Probabilistic UQ Methods with introduction to non-Probabilistic Methods. Cross-listed: (Same as Aerospace Engineering 504 and Biomedical Engineering 504.) |
| ME 517 Finite Elements for Engineering Applications | (3) | Modern computational theory applied to conservation principles across the engineering sciences. Weak forms, extremization, boundary conditions, discrete implementation via finite element, finite difference, finite volume methods. Asymptotic error estimates, accuracy, convergence, stability. Linear problem applications in 1, 2 and 3 dimensions, extensions to non-linearity, non-smooth data, unsteady, spectral analysis techniques, coupled equation systems. Computer projects in heat transfer, structural mechanics, mechanical vibrations, fluid mechanics, heat/mass transport. Cross-listed: (Same as Aerospace Engineering 517.) Comment(s): Bachelor’s degree in engineering or natural science required. Registration Permission: Consent of instructor. |
| ME 518 Computational Fluid Dynamics | (3) | Finite difference and finite volume techniques for solving compressible and incompressible fluid flow problems. Classification of partial differential equations and their discrete approximations. Explicit and Implicit techniques for solving unsteady Euler and Navier-Stokes equations including finite volume and finite difference formulations. Formulation of boundary conditions, artificial viscosity and multigrid acceleration. Stability analysis and convergence. Grid generation. Cross-listed: (Same as Aerospace Engineering 518; Biomedical Engineering 518.) Recommended Background: Fluid mechanics, differential equations, and compressible flows. Registration Permission: Consent of instructor. |
| ME 525 Combustion and Chemically Reacting Flows I | (3) | Fundamentals: thermochemistry, chemical kinetics and conservation equations; phenomenological approach to laminar flames; diffusion and premixed flame theory; single droplet combustion; deflagration and detonation theory; stabilization of combustion waves in laminar streams; flammability limits of premixed laminar flames; introduction to turbulent flames. (DE) Prerequisite(s): 522 and 541 or consent of instructor. |
| ME 570 Numerical Methods for Engineers | (3) | Review and implementation of basic numerical techniques. Explicit and implicit solution techniques of ordinary differential equations and partial differential equations. Applications include heat transfer and fluid mechanics. (Formerly AE 599). Recommended Background: Numerical analysis, fluid mechanics, heat transfer and differential equations. Registration Permission: Consent of Instructor. |
| ME 591 Advanced Engineering Analysis | (3) | Development of weighted residual methods solving for differential, integral and partial differential equations in engineering. Brief introduction to integral equations, asymptotics, functional analysis, orthogonal polynomials and ill-posed problems associated with inverse analysis. Recommended Background: 391 and Mathematics 231. |
| AE 599 Introduction to Computational Aerodynamics | (3) | This course will provide an introduction to computational aerodynamics with a focus on commonly used computational fluid dynamics (CFD) approaches. The theoretical bases of these methods, which generally place an emphasis on the solution of inviscid and/or high-Reynolds number flow fields, will be presented and augmented through application-oriented activities using a CFD solver. Special topics course: must be "Introduction to Computational Aerodynamics." Repeatability: May be repeated. Maximum 6 hours. |
| ME 599 Special Topic: Advanced Manufacturing Processes | (3) | Manufacturing can be loosely divided into those activities carried out by the production engineer and those completed by the industrial engineer. The production engineer is primarily concerned with machines and processes required to produce the desired part(s), while the industrial engineer handles the necessary management and organization activities. In this course, we will focus on the production engineering aspects of manufacturing. |
| ME 617 Discontinuous Galerkin Finite Element Methods | (3) | Comparison and conventional and discontinuous Galerkin (DG) methods; Interior penalty method; Concept of numerical flux; DG formulation of elliptic PDEs: Weighted residual statement and overview of numerical fluxes; Relation between DG and interior penalty methods; Parabolic PDEs: DG formulation and keys to the stability of the method, numerical fluxes; Hyperbolic PDEs: Systems of conservation laws and Rankine-Hugoniot jump conditions; Riemann solutions and numerical fluxes for linear and nonlinear hyperbolic PDEs; Transmitting boundary conditions; Spacetime discontinuous Galerkin methods; Object-oriented design and implementation for DG methods. Cross-listed: (Same as: Aerospace Engineering 617.) |
| ME 655 Advanced Topics in Computational Fluid Dynamics | (3) | Modern approximation theory for Euler and Navier-Stokes conservation systems, compressible flow, hyperbolic forms, boundary conditions. Weak forms, extremization, finite element/finite volume/flux vector discrete implementations, a priori error estimates, accuracy, convergence, stability. Numerical linear algebra, approximate factorization, sparse matrix methods. Dissipation, Fourier spectral analysis, smooth and non-smooth solutions. Cross-listed: (Same as Aerospace Engineering 655.) |
| Course Title | Credit Hours | Description |
|---|---|---|
| MATH 405 Models in Biology | (3) | Difference and differential equation models of biological systems. Credit Restriction: May not be applied toward graduate degree in Mathematics. (DE) Prerequisite(s): 142 or 148 or 152. |
| MATH 411 Mathematical Modeling | (3) | Construction and analysis of mathematical models used in science and industry. Projects emphasized. Recommended Background: Courses in differential equations and linear algebra. |
| MATH 424 Stochastic Processes | (3) | Markov chains, Poisson processes and Brownian motion. Other topics as selected by instructor. (DE) Prerequisite(s): 423. |
| MATH 453 Matrix Algebra II | (3) | Advanced topics in matrix theory including Jordan canonical form. (DE) Prerequisite(s): 251 or 257. |
| MATH 471 Numerical Analysis | (3) | Introduction to computation, instabilities, and rounding. Interpolation and approximation by polynomials and piecewise polynomials. Quadrature and numerical solution of initial and boundary value problems of ordinary differential equations, stiff systems. Cross-listed: (Same as Computer Science 471.) Recommended Background: Course in basic numerical methods. |
| MATH 472 Numerical Algebra | (3) | Direct and iterative methods for systems of linear equations. Solution of single nonlinear equation and nonlinear systems. Orthogonal decomposition, least squares and algebraic eigenvalue problem. Cross-listed: (Same as Computer Science 472.) Recommended Background: Course in basic numerical methods and linear algebra. |
| MATH 513 Mathematical Principles of Fluid Mechanics I | (3) | Equations of motion, incompressible and compressible potential flow, shock waves, viscous flows. Navier-Stokes equations. Recommended Background: Advanced courses in ordinary and partial differential equations and advanced calculus. |
| MATH 515 Analytical Applied Mathematics I | (3) | Analysis of advanced techniques in modern context for applied problems: dimensional analysis and scaling, perturbation theory, variational approaches, transform theory, wave phenomena and conservation laws, stability and bifurcation, distributions, integral equations. Recommended Background: Courses in advanced calculus, linear algebra, and either advanced differential equations or 512. |
| MATH 516 Analytical Applied Mathematics II | (3) | Continuation of 515. (DE) Prerequisite(s): 515. |
| MATH 517 Mathematical Methods in Physics I | (3) | Cross-listed: (See Physics 571.) |
| MATH 518 Mathematics in Industry | (3) | Modeling, analysis, and computation applied to scientific/technical/industrial problems. The course will be centered around several modules, each motivated by a type of mathematical problem frequently occurring in industry. Recommended Background Undergraduate courses in differential equations, linear/matrix algebra, and experience with programming. |
| MATH 525 Statistics I | (3) | Formulation of statistical models, sufficiency; methods of estimation and optimal theory, asymptotic efficiency; the confidence procedures and hypothesis testing, uniformly most powerful tests; Bayesian statistics. Recommended Background: One year of advanced calculus and 425. |
| MATH 527 Stochastic Modeling | (3) | Variable topics in probability applied to real world situations. Topics may include queuing theory, branching processes, Monte Carlo simulation, stochastic finance and other topics as selected by instructor. Recommended Background: One year of advanced calculus and one year of undergraduate probability or mathematical statistics. |
| MATH 537 Mathematical Principles of Continuum Mechanics I | (3) | Conservation principles, equations of equilibrium and motion for fluids and elastic solids, constitutive relations and stress, convexity properties, bifurcation phenomena, existence theory. Recommended Background: Courses in advanced calculus and advanced differential equations. |
| MATH 571 Numerical Mathematics I | (3) | Direct and iterative methods for linear systems. The algebraic eigenvalue problem and the singular decomposition theorem. Newton and quasi-Newton methods for systems of nonlinear equations. Cross-listed: (Same as Computer Science 571.) Recommended Background: Courses in advanced calculus and basic numerical analysis. |
| MATH 572 Numerical Mathematics II | (3) | Numerical techniques for initial value problems of ordinary differential equations. Two-point boundary value problems. Finite difference and finite element methods for selected partial differential equations. Fast Poisson solvers. Cross-listed: (Same as Computer Science 572.) (DE) Prerequisite(s): 571. |
| MATH 577 Optimization | (3) | Mathematical foundations of constrained and unconstrained optimization. Lagrange multipliers, the Farkas lemma, the Kuhn-Tucker-Karush theorem. Analysis of major algorithms and applications to real world problems. Recommended Background: Courses in numerical algorithms, linear algebra and advanced calculus. |
| MATH 578 Numerical Methods for Partial Differential Equations | (3) | Numerical approximation of solutions of partial differential equations including conservation laws and hyperbolic, parabolic, and elliptic problems. Derivation, physical meaning, and implementation of schemes. Recommended Background: A course in partial differential equations or 512 or 515, and familiarity with an operating system and a programming language. |
| MATH 585 Optimal Control Theory | (3) | Deterministic optimal control. Examples involving calculus of variations, optimal trajectories, and engineering control problems. Introduction to stochastic control. Recommended Background: One year of advanced calculus and undergraduate differential equations. |
| Course Title | Credit Hours | Description |
|---|---|---|
| MICR 540 Genomics and Bioinformatics | (3) | Fundamentals of a new scientific discipline based on sequencing genomes (entire DNA) of individual organisms. Goals, principles and types of genome analysis are covered in a traditional lecture course. Computational tools for genome analysis (bioinformatics) are presented in both lecture and hands-on (computer-laboratory) settings. |
| MICR 575 Reproducible Data Analysis | (3) | Introduction to the practice of analyzing and communicating data analysis clearly, efficiently, and reproducibly. Topics include an introduction to functional programming, data visualization, literate programming, and version control. |
| MICR 670 Advanced Topics in Environmental Microbiology | (1-3) | IGMCS eligibility is subject to course topic. Section numbers of eligible courses will be listed as they become available. Repeatability: May be repeated. Maximum 12 hours. Registration Permission: Consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| MSE 504 Special Topics: Introduction To Machine Learning for Materials Science and Engineering | (3) | ML and Artificial Intelligence (AI) have become the defining features of our era, revolutionizing domains from medicine to autonomous driving. In the past decade alone, ML has rapidly advanced dominating in silico applications such as image analysis, recommender systems, and the development of large language models. Now, envision the forthcoming decade as the era of ML's impact on real-world challenges. With ML, we can unlock new frontiers in materials and process optimization, pave the way for groundbreaking materials discovery, and even delve into the intricacies of physics at the nanoscale. This course is designed to equip you with the fundamental knowledge of ML methods while delving deep into the realm of practical applications. By enrolling in this course, you will embark on an exciting exploration of ML's potential in solving real-world scenarios. Discover how ML can revolutionize Materials Science, unraveling complex problems and uncovering innovative solutions. |
| MSE 510 Mathematical and Numerical Problem Solving Skills for Materials Scientists and Engineers | (3) | Formulation and solution of problems in materials science, including linear and nonlinear algebraic equations, ordinary and partial differential equations, and integral equations. Emphasize on use of modern computational tools. Cross-listed: (Same as CBE 506. Formerly MSE 506) |
| MSE 612 Computational Plasticity and Micromechanics | (3) | Computational modeling and simulation methods will be introduced with applications in plasticity, fracture and fatigue, microstructural evolution, and material instability in engineering structural materials. Topics include the classic finite element method based on constitutive modeling, cohesive interface model, discrete dislocation dynamics, atomistic/continuum coupling techniques, and current research areas that are pertinent to the research efforts at UT and ORNL. |
| MSE 613 Modeling and Simulation in Materials Science and Engineering I. Quantum Mechanics | (3) | Introduction to and applications of quantum mechanical modeling and simulation of advanced materials at electronic and atomic levels of description. Development of structure/property relationships for functional, structural, and energy materials. Registration Restriction(s): Minimum student level – graduate. |
| MSE 614 Modeling and Simulation in Materials Science and Engineering II. Classical Mechanics | (3) | Introduction to and applications of classical modeling and simulation of advanced materials at atomic and mesoscale levels of description. Development of structure/property relationships for functional, structural, and energy materials. Registration Restriction(s): Minimum student level – graduate. Registration Permission: Consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| NE 470 Nuclear Reactor Theory I | (3) | Fundamentals of reactor physics relative to cross sections kinematics of elastic scattering, reactor kinetics, reactor systems, and nuclear data. Analytical and numerical methods applicable to general criticality problems, eigenvalue searches, perturbation theory, and the multigroup diffusion equations. |
| NE 571 Reactor Theory and Design | (3) | Analytical and numerical techniques for neutronics modeling of nuclear systems. Forward and adjoint Boltzmann transport equation. Multigroup diffusion theory. Core analysis methods and codes. (RE) Prerequisite(s): 470 or consent of instructor. |
| Course Title | Credit Hours | Description |
|---|---|---|
| PHYS 513 Problems in Theoretical Physics I | (3) | Fundamentals of physics: classical mechanics (Newtonian mechanics, Lagrangian and Hamiltonian dynamics) and electrostatics and magnetostatics. Note: the above course is available online for Fall 2020. |
| PHYS 514 Problems in Theoretical Physics II | (3) | Fundamentals of physics: electrodynamics, relativity, and quantum mechanics. |
| PHYS 571 Mathematical Methods in Physics I | (3) | Linear vector spaces, matrices, tensors, curvilinear coordinates, functions of a complex variable, partial differential equations and boundary value problems, Green's functions, integral transforms, integral equations, spherical harmonics, Bessel functions, calculus of variations. Cross-listed: (Same as Mathematics 517.) Recommended Background: Advanced calculus and differential equations. |
| PHYS 573 Numerical Methods in Physics | (3) | Numerical methods for solution of physical problems, use of digital computers, analysis of errors. (DE) Prerequisite(s): 571 or consent of instructor. |
| PHYS 642 Advanced Topics in Modern Physics | (3) | IGMCS Eligible Topics: "Modern Biological Physics: Network and Systems Biology" |
| PHYS 642 Quantum Computing and Quantum Information | (3) | Content will be chosen from a collection of both fundamental and advanced topics in Quantum Computing and Quantum Information. The fundamental ideas of Quantum mechanics that form the basis of building quantum computers will also be taught as part of this course. Additionally, fundamental Ideas that have influenced the development of classical computers and communication will be presented and contrasted with their quantum counterparts in order to demonstrate the potential power of quantum computing. The course will be self-contained. Cross listed: same as ECE 692. |
| PHYS 643 Computational Physics | (3) | Developing computer algorithms for solving representative problems in various fields of physics, celestial dynamics in astrophysics, boundary value problems in electromagnetism, atomic and nuclear structures, band structure in solid state physics, transport problems in statistical mechanics, Monte Carlo simulation of liquids, fitting and interpolation of data, correlation analysis, or optimization strategy. (DE) Prerequisite(s): 521, 531, and 571. Registration Restriction(s): Minimum student level – graduate. |
| Course Title | Credit Hours | Description |
|---|---|---|
| STAT 537 Statistics for Research I | (3) | Principles and application of statistical methodology, integrated with considerable use of major statistical computing system. Probability and probability distributions, forming and testing hypotheses using parametric and nonparametric inference methods. Matrix-based simple linear regression and correlation. |
| STAT 538 Statistics for Research II | (3) | General linear model as applied to multiple regression and analysis of variance. Diagnostic and influence techniques. One-way, factorial, blocking, and nested designs, preplanned versus post-hoc contrasts. Random factors and repeated measures. (RE) Prerequisite(s): 537 or 532. |
| STAT 563 Probability and Mathematical Statistics | (3) | Basic probability and probability models; random variables and distributional models; kernel density estimation; cubic splines; likelihood inference and maximum likelihood estimation and model fitting with information criteria; moment and moment generating functions; functions of random variables; goodness of fit tests and quantile modeling of distributions. (DE) Prerequisite(s): Mathematics 241. Registration Permission: Prerequisite (s) or consent of instructor required. |
| STAT 564 Statistical Inference II | (3) | Sampling distributions; point and interval estimation; fixed width entropy confidence intervals; likelihood theory; Fisher information and its inverse; large sample, deviance, and bootstrap confidence intervals; Bayesian estimation and hypothesis testing; information approach to hypothesis testing; uniformly most powerful and likelihood ratio tests, theory of linear models, estimation, model building and inference. (DE) Prerequisite(s): 563. |
| STAT 577 Data Mining Methods and Applications | (3) | Understanding and application of data mining methods. Data preparation; exploratory data analysis and data visualization; predictive modeling using generalized linear models, decision trees, neural networks; model assessment; cluster analysis; association analysis; and other topics. Use of standard computer packages. Registration Restriction(s): Minimum student level – graduate. |
| STAT 579 Applied Multivariate Methods | (3) | Multivariate techniques: Hotellings T-sq. MANOVA, discriminant analysis, canonical correlation, principal component analysis, and factor analysis. Computer oriented approach: analysis and interpretation. Knowledge of basic matrices and SAS essential. (DE) Prerequisite(s): 538 or knowledge of regression and analysis of variance. |
| Course Title | Credit Hours | Description |
|---|---|---|
| STEM 591 Visualizing Data Using R | (3) | Intended to support students to create static visualizations (e.g., visualizations for inclusion in presentations and publications) and dynamic visualizations (e.g., those that can allow researchers and others to interact with the visualization). Will use educational examples and data sets but is open to students across programs. Course involves the use of the statistical software R. |
| Course Title | Credit Hours | Description |
|---|---|---|
| EPP 531 Statistical Genetics and Genomics | (3) | Statistical concepts for analysis of genetic and -omics data using classical and contemporary analytical methods. Basic unix scripting and R programming, as well as fundamental genetics and -omics principles will be taught for methods to make sense. Class activities will include lectures, review of literature, and hands-on experience with real data set. Topics will include QC and filtering of next-generation sequence data, variant calling and filtering, genetic linkage mapping, QTL analysis, GWAS, genomic prediction, metagenome analysis, and transcriptome analysis. |
| ANSC 571 Design and Analysis of Biological Research | (3) | Experimental design and procedures; selection of experimental units; analysis and interpretation of data; statistical models and contrasts, analyses of variance: covariates, treatment arrangements, mean separation and regression. Cross-listed: Same as Plant Sciences 571. Recommended Background: 3 hours of statistics. |
| CEM 601 Advanced Epidemiology | (3) | Epidemiological study design, data analysis, and model building. Emphasis placed on using, understanding, and making inferences based on least squares, logistic, Poisson, survival, and mixed models. STATA will be used as the basic computing language for all analyses. Recommended Background: Graduate-level epidemiology course or statistics course. Comment(s): Consent of instructor. Registration Restriction(s): Minimum student level – graduate. |
| CEM 602 GIS and Geographical Epidemiology | (3) | Principles and applications of Geographical Information Systems (GIS) and geographical epidemiology in human and animal health research and practice. Exposure to a wide range of spatial analysis techniques useful in the investigation of human and animal disease problems as well as vector dynamics. The knowledge gained is useful in guiding disease prevention and control strategies. Recommended Background: Graduate-level epidemiology course or statistics course. Comment(s): Consent of instructor. Registration Restriction(s): Minimum student level – graduate. |
| EPP 622 Bioinformatics Applications | (3) | Fundamental bioinformatics concepts, principles and techniques with a focus on the application of bioinformatics to problems in agriculture. Laboratory practical will be taught within a LINUX computational environment where students will gain basic skills in bash and python scripting and construction open source-software based workflows to analyze genomic data. Contact Hour Distribution: 2 hours lecture and 2 hours lab. (DE) Prerequisite(s): Life Sciences 520 or introductory genetics course. Registration Restriction(s): minimum student level – graduate. |