Projects

2018

Fall Semester

CSCI 591, ST: Adv Client-sd Web Prog

Michael Cassens

Course Description:

Offered intermittently. Prereq., CSCI 136. Programming and software development techniques for developing client side web-based applications. Scripting and other programming languages that are used for client-side web-based development. Level: Graduate.

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CSCI 444, Data Visualization

Jesse Johnson

Course Description:

Offered intermittently. Prereq., M 171; programming experience; and junior, senior, or graduate status; or consent of instr. This course emphasizes the practice of data visualization, compelling students to identify and master tools to produce visualizations of data having relational, raster, vector, geographical, networked, mesh based, spectral, or phase space properties. The unique challenges of each data type will be covered. The emphasis on production is coupled to a framework for critique, allowing students to differentiate between high and low qualities visualizations.

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CSCI 558, Intro to Bioinformatics

Oliver Serang

Course Description:

Offered Autumn. Designed for attendance by both computer scientists and biologists. The course will explore the interdisciplinary nature at the juncture of the two fields. Students will be introduced to bioinformatics (emphasis: computational genomics), with exposure to fundamental problems, algorithms, and tools in the field. This includes a basic introduction to genomics, along with in-depth coverage of algorithms and methods relevant to modern computational genomics, including: biological sequence alignment, sequence database homology search, and phylogeny inference. The programming expectations are limited for a 400-level computer science course, but at least one semester of a programming-intensive course is required. Credit not allowed for CSCI 558 and this course.

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2019
Spring Semester

CSCI 591, ST: Computer Vision

Doug Brinkerhoff

Course Description:
Offered intermittently. Prereq., M 221 and CSCI 232, or consent of instructor. Introducing how digital images can be used for quantitative analysis and in artifical intelligence. Topics include the fundamentals of image formation and optics, the geometry of images, feature detection and matching, stereo vision and photogrammetry, tracking, and object detection and classification. Course co-convenes with CSCI 552. Credit not allowed for both CSCI 442 and CSCI 552.
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CSCI 591, ST: Database Security

Michael Martin

Course Description:
(R-12) Offered autumn and spring. Prereq., consent of instr. Experimental offerings of visiting professors, experimental offerings of new courses, or one-time offerings of current topics. Level: Graduate. An overview of both the theory of and applications for providing effective security in database management systems. Topics include conceptual frameworks for discretionary and mandatory access control, data integrity, availability, secure database design, data aggregation and data inference. Models for multilevel secure databases for both relational and object-relational databases are analyzed. Assignments focus on database security concepts and require use of a remote access laboratory.
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M 584, Topics in Combin and Optim

Cory Palmer

Course Description:
(R-12) Offered spring odd-numbered years. Prereq., consent of instr. Topics chosen from the areas of combinatorics and optimization. May include classical problems, current trends, research interests or other topics chosen by the instructor. Level: Graduate.
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Fall Semester

CSCI 591, ST: Cyber Security

Oliver Serang

Course Description:
(R-12) Offered intermittently. Prereq., consent of instr. Experimental offerings of visiting professors, experimental offerings of new courses, or one-time offering of current topics. Level: Graduate. Students will learn how modern computers try to secure themselves, how these systems can be vulnerable, and how to break into systems. Students will learn basic analysis of executables, permissions, x86-64 assembly, exploits (e.g., stack smashing, dangling pointer), defenses to standard exploits, keylogging, logic bombs, malware (and quines), cryptography, breaking hashes (and other penetration testing), online anonymity, and a brief history of how these topics arise and affect our modern world.
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CSCI 547, Machine Learning

Doug Brinkerhoff

Course Description:
Offered intermittently. Prereq., CSCI 232 or consent of instr. Introduction to the framework of learning from examples, various learning algorithms such as neural networks, and generic learning principles such as inductive bias, Occam's Razor, and data mining. Credit not allowed for both CSCI 447 and CSCI 547.
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2020

Spring Semester

CSCI 491, ST: Digital Entrepreneurship

Rob Smith

Course Description:
(R-12) Offered intermittently. Prereq., consent of instr. Experimental offerings of visiting professors, experimental offerings of new courses, or one-time offering of current topics. Level: Graduate. This course teaches students how to successfully launch a high-tech startup. Students will learn how to research and develop product idea using customer discovery, adpapt coding skills to the unique challenges of a startup environment, design and implement a minimum viable product, mitigate the risk of a startup environment, enumerate how non-technical challenges impact technical outcomes, and find, train, and employ co-founders and employees in a startup environment.
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CSCI 543, Human-Computer Interaction

Melissa Holmes

Course Description:
Offered intermittently. Prereq., CSCI 232 or consent of instr. Principles of good design for interactive systems and web-based applications. User-centered design methodology including requirements specification, low and high-fidelity prototyping, heuristic evaluation, cognitive walkthrough, predictive modeling, and usability testing. Advanced HCI research project. Credit not allowed for both CSCI 443 and CSCI 543. Level: Graduate.
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CSCI 592, Independent Study: Computational Medicine

Travis Wheeler

Course Description:
(R-12) Offered autumn and spring. Prereq., consent of instr. Course material appropriate to the needs and objectives of the individual student. Level: Graduate.
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CSCI 591, ST:Proj in Nat Lang Processing

Oliver Serang

Course Description:
(R-12) Offered intermittently. Prereq., consent of instr. Experimental offerings of visiting professors, experimental offerings of new courses, or one-time offering of current topics. Level: Graduate. Students will learn the basics of processing natural languages for text analysis and compiler design. Students will learn about methods for representing text, estimating distance between texts, attributing authorship, parsing a programming language, classifying text, and generating text.
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