Online Master of Science in Computer Science (Online MSCS)

Theoretical and practical aspects of machine learning techniques related to pattern recognition. Statistical methods studied include Bayesian and linear classifiers, support vector machines, neural networks, and unsupervised learning. Syntactic methods include grammatical inference, string matching, and Markov chains. Ensemble methods include random forests, adaptive boosting, and classifier fusion.

An exploration of the central issues in computer architecture: instruction set principles and design, memory hierarchies (cache and main memories, mass storage, virtual memory) and design, pipelining, instruction-level parallelism, bus organization, RISC (Reduced Instruction Set Computers), CISC (Complex Instruction Set Computers), multiprocessors, implementation issues, technology trends, architecture modeling, and simulation.

Operating system structures, virtual memory, interrupts, exceptions, system calls, and processes. Focuses on design and implementation of advanced concepts in file systems, memory management, mass storage devices, process management, and other operating system-related topics.

Theoretical and applied aspects of artificial intelligence. Course topics include problem solving and search, knowledge representation and reasoning, decision-making under uncertainty, machine learning, and multi-agent systems.

Advanced coverage of software processes and technologies that can be used on large projects to help design, manage, maintain, and test software.

Topics in compilers and runtime systems, including: static and dynamic program analysis, performance measurement and characterization, compiler optimization, high-level language virtual machines, instruction set emulation, JIT compilation, explicit vs. automated memory management, and garbage collection.

Recommended Background: Introductory coursework in computer architecture, operating systems, and compilers.

In-depth study on the underlying mathematics, computational methodologies, associated theoretical considerations, applications and practical cryptographic techniques. Hands-on assignments will be given, requiring demonstration, presentation, and report writing.

Credit Restriction: Students cannot receive credit for both 483 and 583.

In-depth study on vulnerabilities and threat vectors associated with mobile and embedded devices, such as smartphones, wearable devices, and IoT devices. Topics include security features and limitations of mobile operating systems, secure programming, mobile location and activity privacy, user/device authentication, IoT wireless security, side-channel attacks on mobile/wearable devices, and security and privacy breaches on smart home devices. Coursework focuses on improving knowledge and awareness of mobile and embedded systems’ security issues, through individual homework assignments and an intensive group project involving hands-on research.

An in-depth introduction to software security. The focus is on identifying vulnerabilities in software, exploiting vulnerabilities in software, and software development best practices for avoiding vulnerabilities during the design, implementation, testing, and deployment of software. Coursework involves hands-on experience exploiting software vulnerabilities to increase understanding, awareness, and appreciation of software vulnerabilities.

Analysis of algorithms and relevance of analysis to design of efficient computer algorithms. Sorting, searching, graph algorithms, pattern matching, dynamic programming, and efficient approximation algorithms.

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, and hierarchical and parallel acceleration methods. The design and use of leading production visualization packages will also be covered.

In-depth overview to the design and implementation of computer networks. It features a top-down approach in the discussion from the application layer down to the physical layer. Topics to be covered include: layered network architecture, physical media, data link protocols, network routing, transport layer, network programming interfaces such as sockets, and applications. Case studies on protocols will be primarily based on the Internet and the TCP/IP suite. Includes hands-on programming assignments related to networking protocols and concrete application problems.

Advanced coverage of the design, implementation, and study of user interfaces. Provides an overview of the user interface design/evaluation process, event abstraction within user interfaces, user interface components, specification of user interfaces, and the architectures within which user interfaces are developed.

In-depth study on core internet and wireless technologies, related security concerns, common security vulnerabilities, and good security practices. Hands-on experience exploiting network protocols and communications, and setting up secure network connections. Hands-on assignments will be given, requiring demonstration, presentation, and report writing.

Provides a practical introduction to big data mining and analytics, blending theory and practice. Over the course of the semester, students will become familiar with modern data science methods, gain comfort with the tools of the trade, explore real-world data sets, and leverage the power of high performance and cloud resources to extract insights from data. Upon completing the course, students will learn how to create reproducible and explanatory data science workflows, how to implement a modern MapReduce, and how to implement parallel clustering methods. Students will also study strategies for overcoming the common imperfections in real-world datasets, and apply their new skills to extract insights from a high-dimensional dataset.

An advanced topic course focused on developing multi-disciplinary skills of discovering, retrieving, analyzing, and presenting operational data. Students will use critical thinking and intense practice solving real-world problems to recognize and address key operational issues: the lack of context, missing observations, and incorrect values. At the end of the course students will be able to discover operational data, to retrieve and store it, to recover context, to estimate the impact of missing events, to identify unreliable or incorrect values, and to present the results. Hands-on assignments will be given, requiring demonstration, presentation, and report writing.

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.

Introduction to database theory, models, and query formation. Survey of scripting languages, their uses, and their interconnectivity with databases.

A broad introduction to human-computer interaction (HCI) and its impact on the theory and practice of cybersecurity. Topics include the research methods in HCI, ethics, authentication, usable secure communication, phishing, usable cryptographic libraries, inclusivity/diversity in system design, and human-aware policy creation. Coursework focuses on helping students adapt a human-aware mindset regarding the design, implementation, and use of cybersecurity tools and principles.

This course presents natural language processing (NLP) theory and methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and enables numerous applications of computational text analysis. The course will cover major algorithms used in NLP, common applications of NLP, and the underlying linguistic phenomena NLP systems attempt to model.

An in-depth study on the principles of designing and developing applications in the context of the Internet and the web. Includes an overview of web protocols, the client-server architecture paradigm, and software components that underlie the two of them. Related topics are also covered, including security, synchronization, interactivity, and advanced web architectures.

Principles and methods for reinforcement learning and sequential stochastic control; Markov decision problems; dynamic programming; temporal difference learning; design considerations for hardware and software.

In-depth study on a range of new developments for the design of secure and trustworthy computer hardware. Topics covered include physical and invasive attack models, side-channel analysis (SCA) attacks, physical unclonable functions, hardware-based random number generators, watermarking of intellectual property (IP) blocks, FPGA security, passive and active metering for piracy prevention, and hardware Trojan detection and isolation. Hands-on assignments will be given, requiring demonstration, presentation, and report writing.

Principles of computer vision algorithms including segmentation and active contour, feature detection and matching, depth recovery from stereo, motion estimation and tracking, image classification, object detection, and scene understanding.