UVa Class Schedule - Schedule for CS 4501 - Fall 2022 (Unofficial, Lou's List)

UVa Class Schedules (Unofficial, Lou's List v2.10) New Features

Schedule for CS 4501 - Fall 2022

These data were not obtained from SIS in real time and may be slightly out of date. MouseOver the enrollment to see Last Update Time

I continue to maintain this list of classes, now with UVA support! -- Lou Bloomfield, Professor Emeritus of Physics

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Computer Science
CS 4501 Special Topics in Computer Science

	Privacy in the Internet Age
16620	001	Lecture (3)	Open	86 / 92	Yixin Sun	MoWe 2:00pm - 3:15pm	Thornton Hall E316
	Prerequisite: CS 2150 or CS 3130 with a grade of C- or above
	Computational Biology / Biological Computing
	Computational Biology / Biological Computing
16629	002	Lecture (3)	Open	47 / 70	David Evans	TuTh 12:30pm - 1:45pm	Olsson Hall 120
	Cybersecurity and Elections
16819	003	Lecture (3)	Open	49 / 50	Jack Davidson+2	MoWe 3:30pm - 4:45pm	Olsson Hall 005
	CS 3710 is a prerequisite for this course. This course is required for the VA Cyber Navigator Internship Program for Summer 2023. If you are interested in the internship and this course is full - please contact Angela (angelao@virginia.edu) to register. Those pursuing the internship will receive priority.
	Cryptocurrency
18804	004	Lecture (3)	Open	79 / 100	Aaron Bloomfield	MoWe 2:00pm - 3:15pm	Thornton Hall E303
	The pre-req is CS 2150 (PDR) or CS 3100 (DSA2) with a grade of C- or above.
	Robotics for Software Engineers
18805	005	Lecture (3)	Open	55 / 70	Sebastian Elbaum	MoWe 3:30pm - 4:45pm	Olsson Hall 011
	The pre-req is CS 3100 (DSA2) or equivalent with a grade of C or above.
	Data Privacy
18806	006	Lecture (3)	Open	33 / 64	Tianhao Wang	MoWe 3:30pm - 4:45pm	Mechanical Engr Bldg 341
	Machine Learning in Image Analysis
19741	007	Lecture (3)	Open	5 / 10	Miaomiao Zhang	MoWe 3:30pm - 4:45pm	Thornton Hall E303
	Introduction to Reinforcement Learning
20055	008	Lecture (3)	Open	22 / 50	Hongning Wang	TuTh 9:30am - 10:45am	Thornton Hall E303
	Statistical Learning and Graphical Models
20180	009	Lecture (3)	Open	7 / 20	Farzad Hassanzadeh+1	MoWe 2:00pm - 3:15pm	Mechanical Engr Bldg 339
	The course is focused on the foundations of estimation theory, probabilistic models for machine learning including graphical models, statistical learning, and relevant computational algorithms. Prerequisites are fluency in basic probability and familiarity with linear algebra. Please see the website for more information.