56:198:500 Introduction to Programming for Computational Scientists (3 credits) 
This course introduces the basics of modern computer programming to beginning graduate students without a background in computer science. Topics covered are: control statements; arrays and lists; classes, objects and methods; inheritance; polymorphism; exception handling; file streams and serialization; recursion; searching and sorting. Students are required to use an up-to-date integrated development environment (IDE) to complete a number of programming assignments.
Prerequisites: None

56:198:501 Data Structures and Algorithmic Problem Solving in Python (3 credits) 
Introduction to algorithms, data structures, and algorithmic paradigms: binary search trees, hashing, sorting, searching, shortest paths, and dynamic programming.
Prerequisites: 56:198:500 or equivalent

56:198:514 Artificial Intelligence (3 credits) 
The objective of this course is to become familiar with Artificial Intelligence. This course will provide students with an understanding of main concepts of Artificial Intelligence needed for the implementation and performance of the fundamentals of intelligent agents/programs and to understand their applications. It focuses on the theory and algorithms underlying Al, including heuristic approaches and advanced search, inference in first order logic, knowledge representation, probabilistic reasoning, and Bayesian belief network.
Prerequisites: 56:198:501 or equivalent 

50:198:523 Software Engineering (3 credits) 
Principles and techniques for the design and construction of reliable, maintainable, and useful software systems. Software life cycle, requirements specifications, and verification and validation issues. Implementation strategies (e.g., top-down, bottom-up, teams), support for reuse, and performance improvement. A treatment of human factors and user interfaces included.
Prerequisites: 56:198:501 or equivalent 
Term: Fall

56:198:541 Distributed and Cloud Computing  (3 credits)  
This course introduces the concepts, models, implementations, and applications of Cloud Computing and Distributed Systems. Topics will include Cloud Architectures, Technologies, Services, and Security. The course will provide students hands-on experience implementing core cloud services and will allow for a more in-depth exploration with a significant semester project.
Prerequisites: 56:198:501 or equivalent 

50:198:547 Network Security (3 credits)
This course will provide in depth instruction on network security methods and technologies. Today, data is typically connected to networks which then may be connected to the internet. With data being connected with the ability for anyone in the world to be able to access it, it is critical that network security methods are used to allow only permitted people access to that data. This is accomplished through the network design, access control policies, and network technology. This course will provide instruction on how these items are used to protect information. This includes the following topics firewalls, intrusion detection and prevention, virtual private networks, proxies, : remote access protections, data loss prevention systems, network and security management systems, network .
Prerequisite: 56:198:501
Term: Spring

56:198:551 Database Systems (3 credits) 
Relational database theory and practice, including database design. Database concepts, relational algebra, data integrity, query languages, and views.
Prerequisites: 56:198:501 or equivalent 

56:198:554 Machine Learning (3 credits) 
This course provides an overview of machine learning and data mining with a focus on the theory and algorithms underlying a range of tasks including data collection and mining, statistical learning theory and underlying probability theory, decision trees, supervised and unsupervised learning, classification, regression and clustering, deep learning, and the derivation practical solutions using predictive analytics. It will deal with machine learning applications in different fields such as bioinformatics and big data analysis.
Prerequisites: 56:198:501 or equivalent 

56:198:556 Computer Graphics (3 credits) 
Graphics systems and imaging principles, graphics programming using packages like OpenGL, input devices and interactive techniques, animation techniques, geometric transformations and modeling in two and three dimensions, viewing in 2D and 3D, lighting and shading, fundamental graphics algorithms (such as clipping, hidden surface removal, etc.)
Prerequisites: 56:198:501 or equivalent 

56:198:561 Optimization Methods (3 credits) 
This course introduces various methods based on linear programming to solve discrete optimization problems. The topics covered in the course will include introduction to linear programming (LP), network flows, and application of LP-based techniques to solve various optimization problems. 
Prerequisites: None 

56:198:562 Big Data Algorithms (3 credits) 
Study of algorithmic techniques and modeling frameworks that facilitate the analysis of massively large amounts of data. Introduction to information retrieval, streaming algorithms and analysis of web searches and crawls.
Prerequisites: 56:198:501 or equivalent 

56:198:567 Applied Probability (3 credits)
An introduction to probability theory and the modeling and analysis of probabilistic systems with emphasis on applications in computer science, engineering, and data science. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation. Limit Theorems. Bernoulli and Poisson processes. Markov chains. Bayesian estimation and hypothesis testing. Elements of statistical inference.
Prerequisites: 50:640:122 or equivalent

56:198:573 Computational Geometry (3 credits) 
Algorithms and data structures for geometric problems that arise in various applications such as computer graphics, CAD/CAM, robotics, and geographical information systems (GIS). Topics include: point location, range searching, intersection, decomposition of polygons, convex hulls, Voronoi diagrams, and line arrangements.
Prerequisites: 56:198:501 or equivalent 

56:198:575 Crytography and Computer Security (3 credits)
Secret-key cryptography, public-key cryptography, key agreement, secret sharing, digital signatures, message and user authentication, one-way functions, key management; attacks; practical applications to computer and communications security.
Prerequisites: 56:198:501 or equivalent.

56:198:576 Theory of Computation (3 credits)
Formal languages, automata and computability; regular languages and finite-state automata; context-free grammars and languages; pushdown automata; the Church-Turing theses; Turing machines; decidability and undecidability. 
Prerequisites: Permission of instructor. 

56:198:693 Master’s Project (3 credits)
Open only to students pursuing the project option. Design, implementation, and demonstration of a significant software project. Project proposals must be approved by instructor. The project completion requires a report and a presentation.
Prerequisite: Permission of instructor.

56:198:697 Computer Science Internship (3 credits)
The practical application of computer science knowledge and skills through an approved internship in a sponsoring organization. Arrangements for the internship must be agreed upon by the sponsoring organization and approved by the department before the beginning of the semester. Students should consult the department for detailed instructions before registering for this course.
Prerequisite: Approval by department 

56:198:701,702 Research in Computer Science (3 + 3 credits)
Open only to students pursuing the thesis option. This will involve two semesters’ worth of substantial and independent research on a topic approved and supervised by a faculty member (the thesis adviser) who will work closely with the student. This research will be exposited in the student’s M.S. thesis.
Prerequisite: Permission of thesis adviser and graduate director.