This pages serves as a quick reference for current and prospective students on courses taught within the School of CSE. This page is a convenient supplement to, but does not supersede, course listings and descriptions in the Georgia Tech Course Catalog. The schedule of classes found on OSCAR is also useful for seeing when specific courses are offered.

Graduate Courses (CSE)

CSE 6001. Introduction to Computational Science and Engineering. 1 Credit Hour.
This course will introduce students to major research areas in computational science and engineering.

CSE 6010. Computational Problem Solving for Scientists and Engineers. 3 Credit Hours.
Computing principles, computer architecture, algorithms and data structure; software development, parallelism. No credit for graduate students or undergraduates in Computer Science or Computational Media.

CSE 6040. Computing for Data Analysis: Methods and Tools. 3 Credit Hours.
Computational techniques needed for data analysis; programming, accessing databases, multidimensional arrays, basic numerical computing, and visualization; hands-on applications and case studies. Credit is will not be awarded for both CSE 6040 and CX 4240.

CSE 6140. Computational Science and Engineering Algorithms. 3 Credit Hours.
This course will introduce students to designing high-performance and scalable algorithms for computational science and engineering applications. The course focuses on algorithms design, complexity analysis, experimentation, and optimization, for important science and engineering applications.

CSE 6141. Massive Graph Analysis. 3 Credit Hours.
Algorithms and data structures for massive graphs; programming, parallelism; principles, challenges, opportunities in graph analysis; hands-on application, case studies.

CSE 6220. High Performance Computing. 3 Credit Hours.
This course will introduce students to the design, analysis, and implementation of high performance computational science and engineering applications.

CSE 6221. Multicore Computing: Concurrency and Parallelism on the Desktop. 3 Credit Hours.
This course will introduce students to the design and analysis of real-world algorithms on multicore computers.

CSE 6230. High Performance Parallel Computing: Tools and Applications. 3 Credit Hours.
Introduction to MIMD parallel computation, using textbook excerpts, resesarch papers, and projects on multiple parallel machines. Emphasizes practical issues in high-performance computing.

CSE 6236. Parallel and Distributed Simulation. 3 Credit Hours.
Algorithms and techniques used in parallel/distributed discrete event simulation systems. Synchronization algorithms, data distribution, applications to high performance analytic simulations and distributed virtual environments.

CSE 6240. Web Search and Text Mining. 3 Credit Hours.
Basic and advanced methods for Web information retrieval and text mining: indexing and crawling, IR models, link and click data, social search, text classification and clustering.

CSE 6241. Pattern Matching Algorithms. 3 Credit Hours.
Foundations and algorithms underlying the development and application of tools for the efficient searching, matching and discovery of discrete.

CSE 6242. Data and Visual Analytics. 3 Credit Hours.
The course introduces students to analysis and visualization of complex high dimensional data. Both theory and applications will be covered including several practical case studies.

CSE 6243. Advanced Topics in Machine Learning. 3 Credit Hours.
Advanced machine learning topics including graphical models, kernel methods, boosting, bagging, semi-supervised and active learning, and tensor approach to data analysis.

CSE 6250. Big Data Analytics for Healthcare. 3 Credit Hours.
Big data systems, scalable machine learning algorithms, health analytic applications, electronic health records.

CSE 6301. Algorithms for Bioinformatics and Computational Biology. 3 Credit Hours.
Foundations and algorithms underlying the development and application of tools for the efficient management and processing of biomolecular data.

CSE 6643. Numerical Linear Algebra. 3 Credit Hours.
Introduction to numerical solutions of the classical problems of linear algebra including linear systems, least squares, singular value decomposition, and eigen value problems. Crosslisted with MATH 6643.

CSE 6644. Iterative Methods for Systems of Equations. 3 Credit Hours.
Iterative methods for linear and nonlinear systems of equations including Jacobi, G-S, SOR, CG, multigrid, Newton, quasi-Newton, updating, and gradient based methods. Crosslisted with MATH 6644.

CSE 6710. Numerical Methods in Computational Science and Engineering I. 3 Credit Hours.
Introduction to numerical algorithms widely used in computational science and engineering. Numerical linear algebra, linear programming, and applications. Crosslisted with MATH 6710.

CSE 6711. Numerical Methods in Computational Science and Engineering II. 3 Credit Hours.
Efficient numerical techniques for solving partial differential equations and large-scale systems of equations arising from discretization of partial differential equations or variational problems in applications in science and engineering. Crosslisted with MATH 6711

CSE 6730. Modeling and Simulation: Foundations and Implementation. 3 Credit Hours.
Foundations and algorithms concerning the development of conceptual models for systems, and their realization in the form of computer software; discrete and continuous models. Crosslisted with ECE 6730.

CSE 6740. Computational Data Analysis: Learning, Mining, and Computation. 3 Credit Hours.
Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both resesarch and practice. Crosslisted with ISYE 6740. Credit not awarded for both CSE 6740 and CS 4641/7641/ISYE 6740.

CSE 6742. Modeling, Simulation and Military Gaming. 3 Credit Hours.
Focuses on the creation and use of modeling and simulation tools to analyze and train students regarding strategic events in international relations. Crosslisted with INTA 6742.

CSE 6748. Applied Analytics Practicum. 6 Credit Hours.
Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization.

CSE 7750. Mathematical Foundations of Machine Learning. 3 Credit Hours.
Provides the mathematical background for two of the pillars of modern data science: linear algebra and applied probability.

CSE 7751. Probabilistic Graphical Models in Machine Learning. 3 Credit Hours.
The course provides an introduction to theory and practice of graphical models in machine learning. It covers three main aspects; representation, probabilistic inference, and learning.

CSE 7850. Machine Learning in Computational Biology. 3 Credit Hours. (Luo)
This course focuses on the intersection between machine learning and computational biology. It covers modern machine learning techniques, including supervised and unsupervised learning, feature selection, probabilistic modeling, graphical models, deep learning, and more.

CSE 8802 IPL. InQuBATE Project Laboratory. 2 Credit Hours. (Cherry) 
This course acts as a bridge between core curricula and thesis development in integrative and quantitative biosciences. 

CSE 8803 ASC. Advanced Scientific Computing. 3 Credit Hours. 
Major computational methods used in science and engineering for numerical simulation, inverse problems, optimization, and uncertainty quantification. 

CSE 8803 BM. Biomedical Modeling. 3 Credit Hours. (Cherry) 
This course provides an introduction to commonly used models and modeling techniques for describing the dynamics of various physiological cells, tissues, and diseases are created, solved, and used. 

CSE 8803 EPI. Data Science for Epidemiology. 3 Credit Hours. (Prakash) 
This course will cover foundations of computational and networked epidemiology and data science/machine learning algorithms and systems in context of public health applications. 

CSE 8803 IDM. Imaging with Data-driven Models. 3 Credit Hours. (Herrmann) 
This course concerns inverse problems as they relate to imaging. Special emphasis will be given to Bayesian Inference with Normalizing Flows, a special type of invertible neural networks, which forms an important development by capturing uncertainty via distribution learning.

CSE 8803 MIP. Materials Informatics. 3 Credit Hours. (Kalidindi) 
This course introduces fundamental principles of the application of data science concepts to problems in multiscale materials design and development. The focus will be on the extraction of high-fidelity, reduced-order, Process-Structure-Property (PSP) linkages that constitute the essential knowledge needed to support all materials innovation efforts.

CSE 8803 DLT. Deep Learning for Text Data. 3 Credit Hours. (C. Zhang) 
This course will introduce state-of-the-art deep learning techniques for text data analysis, with particular emphasis on representation learning, language models, text generation, and knowledge extraction. 

CSE 8803 NDA. Numerical Methods for Data Analytics. 3 Credit Hours. (Park) 

CSE 8803 MLB. Machine Learning for Computational Biology. 3 Credit Hours. (X. Zhang) 
This course focuses on the intersection between machine learning and computational biology. Students will learn the applications of machine learning methods to a variety of biological problems in genomics, single-cell analyses, structural biology, and system biology. 

CSE 8803 DSN. Data Science for Social Networks. 3 Credit Hours. (Kumar) 
This course dives into the advancements in artificial intelligence, applied machine learning, and data science for web, social network, and social media analysis powered by applications of natural language processing and graph neural networks to detect and mitigate online harmful content. 

CSE 8803 CMC. Computational Methods for Complex Systems. 3 Credit Hours. (Imam) 
This course introduces techniques for modeling emergent properties of complex systems using examples drawn from diverse areas of science and engineering. Topics include entropy, emergence, free energies, biological computation, phase transitions, universality and scale invariance.  

CSE 8803 IUC. Introduction to Urban Analytics. 3 Credit Hours. (Prakash) 
This course introduces various computational approaches for addressing the challenges that arise in urban environments, for example, energy management, emergency preparedness, public health and traffic modeling. 

CSE 8803 MLC. Machine Learning for Chemistry and Materials. 3 Credit Hours. (Fung) 
Introduces how modern machine learning methods can be used for chemistry, chemical engineering and the materials sciences, particularly at the atomistic level. Topics will revolve around solving problems in chemical property prediction, physical simulations, and materials design. 

CSE 8803 IUQ. Introduction to Uncertainty Quantification. 3 Credit Hours. (Chen) 
This course introduces computational foundations and various applications of quantitative modeling, propagation, and data-driven reduction of uncertainties, as well as ultimate decision-making under uncertainties through Bayesian inference, stochastic optimization, and machine learning. 

CSE 8803 MLG. Machine Learning with Graphs. 3 Credit Hours. (Luo) 

CSE 8803 SML. Scientific Machine Learning. 3 Credit Hours. (Pestourie) 

CSE 8803 DTP. Digital Twins for Physical Systems. 3 Credit Hours. (Herrmann) 
IBM defines “A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.” This course explores these concepts and their significance in addressing the challenges of monitoring and control of physical systems described by partial-differential equations. 

CSE 8803 CDS. Foundations of Computational Dynamical Systems. 3 Credit Hours. (Chandramoorthy) 

CSE 8803 MOR. Model Reduction. 3 Credit Hours. (Qian) 
Introduces projection-based model reduction methods for surrogate modeling of high-dimensional systems arising from PDEs. Topics covered include proper orthogonal decomposition, reduced basis methods, system-theoretic methods including rational interpolation and balanced truncation, nonlinear model reduction, and data-driven model reduction. 

Undergraduate Courses (CX)

CX 4010. Computational Problem Solving for Scientists and Engineers. 3 Credit Hours.
Computing principles, computer architecture, algorithms and data structures; software development, parallelism. No credit for graduate students or undergraduates in Computer Science or Computational Media.

CX 4140. Computational Modeling Algorithms. 3 Credit Hours.
Design, analysis and implementation of algorithms for modeling natural and engineered systems; algorithm experimentation, and optimization.

CX 4220. Introduction to High Performance Computing. 3 Credit Hours.
Design of algorithms and software for high performance computing platforms used in computational science and engineering. Topics include parallelism, locality, machine architectures, and programming.

CX 4230. Computer Simulation. 3 Credit Hours.
Algorithms and techniques for creating computer simulations and their realization in software.

CX 4232. Simulation and Military Gaming. 3 Credit Hours.
Creation and use of modeling and simulation tools to analyze and train students regarding strategic events in international relations.

CX 4236. Distributed Simulation. 3 Credit Hours.
Algorithms and techniques used to execute simulations on parallel/distributed computing platforms. Simulations for analysis, virtual environments, and computer gaming.

CX 4240. Introduction to Computing for Data Analysis. 3 Credit Hours.
Computational techniques needed for data analysis; programming, accessing databases, multidimensional arrays, basic numerical computing, and visualization; hands-on applications and case studies.

CX 4242. Data and Visual Analytics. 3 Credit Hours.
Introduction to the analysis of complex data; theory, applications and practical case studies.

CX 4640. Numerical Analysis I. 3 Credit Hours.
Introduction to numerical algorithms for some basic problems in computational mathematics. Discussion of both implementation issues and error analysis.

CX 4641. Numerical Analysis II. 3 Credit Hours.
Introduction to the numerical solution of initial and boundary value problems in differential equations.

CX 4777. Introduction to Parallel and Vector Scientific Computing. 3 Credit Hours.
Scientific computational algorithms on vector and parallel computers. Speed-up and algorithm complexity, interprocess communication, synchronization, modern algorithms for linear systems, programming techniques, code optimization.

CX 4803 CML. Computational Foundations of Machine Learning. 3 Credit Hours. 
Introduction to machine learning methods through mathematics and programming. 

CX 4803 MLB. Machine Learning for Computational Biology. 3 Credit Hours. (X. Zhang) 
This course focuses on the intersection between machine learning and computational biology. Students will learn the applications of machine learning methods to a variety of biological problems in genomics, single-cell analyses, structural biology, and system biology. 

CX 4803 WSM. Web Search and Text Mining. 3 Credit Hours. 
Basic and advanced methods for Web information retrieval and text mining: indexing and crawling, IR models, link and click data, social search, text classification and clustering.