CSE Core Courses

AAE 55800 – Finite Element Methods in Aerospace Structures 
The goal of this course is to introduce the theory behind finite element calculations of stress, strain, and deformation in structures and materials and describe the role of a commercial finite element package in structural analysis and design. 

CE 59500 – Finite Elements in Elasticity 
Fundamentals of theory of elasticity; variational principles; one-, two-, and three-dimensional elasticity finite elements; interpolation methods; numerical integration; convergence criteria; stress interpretation. 

CE 59700 – Computational Methods in Civil Engineering 
The objective of the course is to introduce students to numerical methods for solving problems in civil engineering (both for modeling and experimental work). The course provides students with the necessary background to enable them to use basic computational tools and gain a fundamental understanding of numerical methods. It also introduces them to basic computer programming and inculcates a systematic logical thought process towards problem solving. 

CHM 69600 – Mathematical Methods for Chemists 
This math methods course for chemistry graduate students will cover a range of mathematical techniques with a focus on their applications in quantum mechanics. Topics will include linear algebra, eigenvectors, differential equations, partial differential equations, Fourier series and transforms, Laplace transforms, complex analysis, coding in python, machine learning, and optimization. 

CS 51400/MA 51400 – Numerical Analysis 
Iterative methods for solving nonlinear equations; linear difference equations, applications to solution of polynomial equations; differentiation and integration formulas; numerical solution of ordinary differential equations; roundoff error bounds. 

CS 51500 – Numerical Linear Algebra 
Computational aspects of linear algebra; linear equations and matrices, direct and iterative methods; eigenvalues and eigenvectors of matrices; error analysis. 

ECE 64500 – Estimation Theory 
This course presents the basics of estimation and detection theory that are commonly applied in communications and signal processing systems. Applications in communications and signal processing will be considered throughout. 

MA 69200 – Spectral Element Method 
Topics in applied math. 

MA 69200 – Topics Applied Math 
This course will constitute an introduction to the kinetic theory with the focus on the Boltzmann equation and related kinetic models. Will discuss basic mathematical theory, numerical methods, and various applications. 

ME 58100 – Numerical Methods 
This course is designed to introduce the student to the classical numerical methods available for engineering problem solving. At the same time it hopes to familiarize the student with the computer as an engineering tool and to improve programming skills. The emphasis on this course is on fundamental understanding of the methods and the importance of errors that are inevitably associated with scientific computing. 

ME 68100 – Finite and Boundary Element Methods 
The aim of the course is to provide a fundamental treatment of the mechanics principles, element formulation techniques, and numerical solution methods to enable advanced understanding of linear and nonlinear finite element analyses. The course will also provide a brief introduction to boundary element method. The course will enable application experience through homework assignments and projects. 

CS 52500 – Parallel Computing 
Parallel computing for science and engineering applications: parallel programming and performance evaluation, parallel libraries and problem-solving environments, models of parallel computing and run-time support systems, and selected applications. 

CS 51501 – Parallelism in Numerical Linear Algebra 
This course examines both theoretical and practical aspects of numerical algorithm design and implementation on parallel computing platforms. In particular, it provides an understanding of the tradeoff between arithmetic complexity and management of hierarchical memory structures, roundoff characteristics if different from the sequential schemes, and performance evaluation and enhancement. Applications are derived from a variety of computational science and engineering areas. 

ECE 56300 – Programing Parallel Machines 
This course presents methods and techniques for programming parallel computers. Various parallel algorithms are presented to demonstrate different techniques for identifying parallel tasks and mapping them onto parallel machines. Realistic science/engineering applications and their characteristics will be discussed. Parallel architectures to be considered are shared-memory and distributed-memory multiprocessor systems. Programming paradigms for these machines will be compared, including directive-based (such as OpenMP), message passing (such as MPI) and thread-based (such as Posix threads) methods. Methodologies for analyzing and improving the performance of parallel programs will be discussed. There will be a class project in which each student will parallelize and improve the performance of a computational application. Each student will also prepare one lecture on a selected topic. 

STAT 69500 - Divide Recomb RHadoop Big Data 
In this course, which is hands-on, participants will learn (1) basic concepts of D&R; (2) use of the Tessera software system; (3) basics of distributed parallel computational environments, and the Hadoop environment; (4) a rigorous, scientific framework for computational performance measurement and analysis of distributed parallel computational environments. 

BME 64600 – Deep Learning 
This course teaches the foundation of Deep Learning and advanced neural networks for a more experienced audience. 

CS 57800 – Statistical Machine Learning 
During the course, students will learn about different supervised and unsupervised problems, and their related algorithms, implement some of those algorithms, learn the theory behind some algorithms, e.g., geometrical aspects and generalization, and learn algorithm-independent principles, e.g., cross-validation, bias-variance tradeoff. 

ECE 57000 – Artificial Intelligence 
Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence. 

ME 53900 – Introduction to Scientific Machine Learning 
Introduction to the fundamentals of predictive modeling for advanced undergraduates and graduate science and engineering students that work in the intersection of data and theory. 

CGT 62100 – Compute Shader Programming 
Compute shaders allow graphics programmers to leverage the parallel processing power of the GPU for tasks besides rendering. Kinematics and other simulations, collision detection and image processing are common applications. In this class students will use several different shading languages to implement a variety of algorithms in both stand-alone applications and in game engines. 

CS 53000 – Introduction To Scientific Visualization 
Teaches the fundamentals of scientific visualization and computer graphics, and prepares the student for applications of these techniques in his or her field of specialization, including such fields as astronomy, biology, chemistry, engineering, physics, etc. Emphasis shall be on the representation of scalar, vector and tensor fields, data sampling and resampling, and reconstruction using multivariate finite elements (surfaces, volumes and surfaces on surfaces). 

ECE 66100 – Computer Vision 
This course deals with how an autonomous or a semi-autonomous system can be endowed with visual perception. The issues discussed include: sampling from a topological standpoint; grouping processes; data structures, especially hierarchical types such as pyramids, quadtrees, octrees, etc.; graphic theoretic methods for structural description and consistent labeling; issues in 3-D vision such as object representation by Gaussian spheres, generalized cylinders, etc.