Sem:T-Accessible Computing

This course explores how computing can enhance accessibility and how disability studies can guide effective solutions. Students learn to assess and improve the accessibility of documents, websites, apps, and advanced technologies like AR/VR and AI/ML. The course covers practical skills, including evaluating accessibility and implementing inclusive design, and addresses future-oriented topics such as intersectional issues, accessible healthcare, and disaster response.

Sem: Parallel Programming

The field of high-performance computing (HPC) leverages the largest and most powerful computers on the planet to enable cutting edge scientific breakthroughs that help us understand fundamental research questions. These machines and programs push the limits of speed and scalability and require a practical understanding of the entire computing stack as well as familiarity with novel and emerging hardware platforms. In this course, students learn and apply both the theoretical and practical aspects of the field.

Sem:T-BiomedicalBigData

This course explores the intersection of computer science and biomedical research. In the genomic era, biological and clinical research generates vast amounts of omics data, much of which is publicly available. Students examine the scientific literature to learn about ways that researchers are harnessing this data to make new discoveries in biomedical domains. This course also discusses the challenges that biomedical big data presents in terms of storage, access and analysis.

Sem:Digital Circuits&Sensors

Offered as CSC 328 and EGR 328. Previously EGR 390dc. Digital circuits are everywhere, from basic thermostat controls and stop light sequencers to smart phones, computers and even Mars Rovers! This course covers the basic building blocks for all electronics. Students investigate basic logic circuits, combinatorial logic and sequential logic with an introduction to the basic digital circuits such as encoders and multiplexers. The second part of the semester focuses on microprocessors, using the Arduino.

Intro Artificial Intelligence

An introduction to artificial intelligence including an introduction to artificial intelligence programming. Discussions include: game playing and search strategies, machine learning, natural language understanding, neural networks, genetic algorithms, evolutionary programming and philosophical issues. Designations: Theory, Programming. Prerequisite: CSC 210 and MTH 111, or equivalent. Enrollment limited to 30.

Discrete & Comp Geom

Topics include the core of the field: polygons, convex hulls, triangulations and Voronoi diagrams. Beyond this core, curves and surfaces, and computational topology are covered. Throughout, a dual emphasis is maintained on mathematical proofs and efficient algorithms. Students have a choice of concentrating their course work in mathematics or toward computer science. Designations: Theory, Programming. Prerequisites: CSC 210, MTH 111 and MTH 153.

Image Processing Fundamentals

Images fill the media, and most are processed by computer at some point or another. This course examines a variety of algorithmic image processing techniques, exploring implementation and applications, as well as some of the social impact and ethical issues surrounding their use. Prerequisites: CSC 212 and MTH 111. Enrollment limited to 30.

Operating Systems

An introduction to the functions of an operating system and their underlying implementation. Topics include file systems, CPU and memory management, concurrent communicating processes, deadlock, and access and protection issues. Programming projects implement and explore algorithms related to several of these topics. Designations: Programming, Systems. Prerequisite: CSC 231. Enrollment limited to 30.

Algorithms

Covers algorithm design techniques ("divide-and-conquer," dynamic programming, "greedy" algorithms, etc.), analysis techniques (including big-O notation, recurrence relations), useful data structures (including heaps, search trees, adjacency lists), efficient algorithms for a variety of problems and NP-completeness. Designation: Theory. Prerequisites: CSC 210, MTH 111 and MTH 153. Enrollment limited to 30.

Theoretical Foundations

Automata and finite state machines, regular sets and regular languages, push-down automata and context-free languages, linear-bounded automata, computability and Turing machines, nondeterminism and undecidability. Prerequisites: CSC 110 and MTH 153. Enrollment limited to 30.
Subscribe to