Special Topics Courses for Fall 2023
Last Updated: Thursday, March 8
CS Undergraduate Special Topics
CS 4501: Privacy in the Internet Age w/ Prof. Sun
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3130)
This course provides an in-depth look into privacy issues on the Internet and introduces privacy enhancing technologies. We will cover topics such as anonymous communications, traffic analysis, and security topics with privacy implications.
CS 4501: F1/10 Autonomous Racing w/ Prof. Behl
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3140)
Students work in teams to build, drive, and race 1/10th scale autonomous racecars, while learning about the principles of perception, planning, and control for autonomous vehicles. The course culminates in a F1/10 ‘battle of algorithms’ race amongst the teams.
CS 4501: Cybersecurity and Elections w/ Prof. Davidson and Prof. Orebaugh
(3 credits / Prerequisites: C- or better in CS 3170)
CS 4501: Cryptocurrency w/ Prof. Bloomfield
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3100)
This course is meant as a general introduction to cryptocurrency. This course is split into three “modules”: introduction and Bitcoin, Ethereum and smart contracts, and Web3.
CS 4501: Usability Engineering w/ Prof. Apostolellis
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3140)
Usability engineering, a sub-domain of Human Computer Interaction, explores the best ways to design interactive systems that are “easy and intuitive to use.” Although this last phrase has been widely used, or abused, by software engineers and other stakeholders in software development, there is actually a foolproof path to design interactive systems that ensure usability (i.e., usefulness, efficiency, effectiveness, learnability, satisfaction, accessibility).
CS 4501: TBD w/ Prof. Kwon
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3140)
CS 4501: Optimization w/ Prof. S. Zhang
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3140)
The recent phenomenal success of AI is mostly due to the application of giant neural networks. The most effective and widely used method to train those neural networks is gradient descent, which belongs to the family of first-order optimization methods. In this course, we will dive into the foundations of first-order optimization methods to understand why they work and how they work. You will have the chance to prove their efficacy and implement them.
CS 4501: Machine Learning in Image Analysis w/ Prof. M. Zhang
(3 credits / Prerequisites: C- or better in CS 2150 or CS 3140)
This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed to build their own models for effective problem solving. Topics of image denoising/reconstruction, deformable image registration, numerical analysis, probabilistic modeling, data dimensionality reduction, and convolutional neural networks for image segmentation/classification will be covered. The main focus might change from semester to semester. The graduate students (ECE/CS 6501) will be given additional programming tasks and more advanced theoretical questions.
CS Graduate Special Topics
Coming soon!
Special Topics Outside CS
ENGR 4880: Engineering & Business - Leadership and Purpose in a Complex World
(3 credits) Dialogue, discussion, team interactions and a summit of Mt. Everest (virtual) will be complemented with insights from guest speakers. Classes will be hosted by Blair Okita, a UVA Engineering graduate, who has led Process Development, Manufacturing and Quality Organizations at start-ups, mid-size, and multinational biopharmaceutical companies. PDF Flyer