Online Master of Science in Computer Science Program 

This class focuses on several core topics in the design, analysis, and implementation of computational tools that are drawn from the fields of data structures, software engineering, and programming languages. Other topics include object-oriented programming, test-driven development, data structures and abstract data types, imperative programming and memory management, and functional programming.

By solving practical, real-life problems in different programming languages and in different ways, students learn to select a language and approach most appropriate for the situation, and prepare to learn new languages independently. The high-level goal of this course is to train students to be able to draw from a versatile set of skills, which in turn will provide a strong foundation for further study in computer science.

This course covers fundamental algorithm design principles and data structures, basic notions of complexity theory, as well as an advanced introduction to parallel algorithms, randomized algorithms, and approximation algorithms. Topics include: divide-and-conquer, dynamic programming, amortized analysis, graph algorithms, network flow, map reduce, and more advanced topics in approximation algorithms and randomized algorithms. Satisfies the algorithm breadth area requirement.

The objective of the course is to introduce students to the core concepts and analytic techniques in the design and analysis of computer networks and network protocols. We will explain both how computer networks work using the Internet as the paradigm and why they work from an optimization and control perspective. Satisfies the systems breadth area requirement.

The aim of this course is to provide the student with an introduction to the main concepts and techniques playing a key role in artificial intelligence. In addition to covering the primary components of AI, particular attention will be devoted to its applications in several fields. Among the topics covered are: intelligent artificial agents, problem-solving using search and constraint satisfaction, uncertainty, Bayesian networks and probabilistic inference, supervised learning, planning, and sequential decision problems, as well as several additional topics. Satisfies the artificial intelligence/machine learning breadth area requirement.

The objective of the course is to introduce students to the core concepts and analytic techniques in the design and analysis of computer networks and network protocols. We will explain both how computer networks work using the Internet as the paradigm and why they work from an optimization and control perspective.

An introduction on how graphical representations of data can be used to aid understanding. This course details the theory and practice of designing effective information or scientific visualizations. The techniques learned in this class have wide applications to all fields in engineering and science, where due to increasing sizes and complexity, data now demands effective presentation and analysis. Topics will include iso-surfacing, volume rendering, transfer functions, vector/tensor fields, topological analysis, large data visualization, and uncertainty in visualizations.

This course provides an introduction to geometric algorithms and geometric data structures. Computational Geometry is a young discipline which enjoys close relations to mathematics and to various application areas such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems (GIS), VLSI, robotics, computer graphics and geometric modeling. Covered topics include fundamental geometric algorithm design and analysis paradigms, geometric data structures for planar subdivisions and range searching, algorithms to compute the convex hull, Voronoi diagrams, and Delaunay triangulation, as well as selected advanced topics.

A comprehensive introduction to the mathematics and algorithms that drive today’s digital special effects, animation, and games. Designed as a hands-on course, students will gain experience in building 2D/3D interactive applications using OpenGL. Topics covered will include geometric transformations, projections, raster algorithms, 3D object models (surface and volume), visible surface algorithms, texture mapping, lighting/shading, ray-tracing, anti-aliasing, and compositing.

This course is an introductory course on the fundamentals of 3D game development. We will focus on the Unity game engine and object-oriented programming with C#. Students will learn various topics including vectors and rotations, character animation, artificial intelligence, and Unity tools, such as Cinemachine, Shader Graph, and Timeline.

This course covers the design and implementation of operating systems. Operating systems serve as an interface between the hardware of a computer and the software running on it. This course addresses how operating systems enable the sharing of limited resources — CPU, memory, hard disks — robustly, securely, and efficiently amongst all running processes on a computer. CPU virtualization, concurrency, memory virtualization, file systems APIs and implementations, and security are all covered with a focus on the key design ideas and abstractions within the topics. In addition, students get practical experience implementing their own operating system.

This is a project-oriented course on fundamentals of software development and software engineering. Working in teams, students apply a recognized software engineering methodology, a modern programming language, and software development tools (including an IDE, debugger, version control system, and testing framework) to design and implement a semester-long project — a software solution for a real-world problem. The high goal of the course is to train students to function efficiently in a real-world software development environment. To help reach that goal, the students do a lot of independent learning, teamwork, documentation and public presentation of their product and design process. The particular technologies employed in the course may change in synchrony with changes in the software engineering field, currently the focus is on engineering software-as-a-service using Ruby for programming language and Rails for web development framework.

This course has two main goals. The first one is to give a broad overview of the fundamentals of multi-agent systems (MAS). MAS are playing an increasingly important role in artificial intelligence as distributed resources push for highly distributed forms of intelligence. The second aim is to provide a more in-depth discussion of selected MAS topics: game theory and voting from a computational point of view. Situated at the nexus between economics and computer science, these research areas provide a perfect example of interdisciplinary cross-fertilization and mutual enrichment and lie at the core of multi-agent systems theory. The course will provide the student with an understanding of how self-interested behavior and coordination can be formally modeled and implemented in societies of artificial agents. Course may be repeated up to unlimited credit hours.

This course varies from time to time, focusing on topics of interest to the faculty and students. Course may be repeated up to unlimited credit hours.

The aim of this course is to provide the student with an introduction to the main concepts and techniques required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data-driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SOL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing, and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses.

This course investigates computational methods to work with human language, analyzing its lexical, syntactic, and semantic aspects. Examples include document classification and clustering, syntactic parsing, information extraction, speech recognition, and machine translation. Theoretical and practical aspects of the latest techniques will be covered, including probabilistic modeling, neural networks, and deep learning.

This is a hands-on introductory security course for upper-level undergraduate students and graduate students. Students will learn the basics of cryptography and methods for protecting systems from attack. We will cover malicious software and other attacks that occur over the network, as well as the perimeter defenses used to stop these attacks. Students will then learn about program vulnerabilities that lead to most of the security problems in computing today. We will conclude with the other administrative issues that security professionals must consider in their jobs.