CS 1699 SPECIAL TOPICS IN COMPUTER SCIENCE

Description

Minimum Credits: 3
Maximum Credits: 3
This is a special topics course that allows the computer science department to test run a course before deciding whether to permanently add it to our curriculum.
Academic Career: Undergraduate
Course Component: Lecture
Grade Component: LG/SNC Elective Basis

Current Sections

Spring 2020

Class No.
Days
Times
Room
Instructor(s)
TA(s)
Type
Session
Writing
Class No.: 27660 (1115)
Days: TuTh
Times: 3:00 pm - 4:15 pm
Room: PUBHL G23
Instructor(s): A. Kovashka
TA(s):
Type: LEC
Session: SE3
Writing:
Description: DEEP LEARNING. Prerequisites: MATH 0220, MATH 0280, and CS/COE 1501

This course will cover the basics of modern deep neural networks. The first part of the course will introduce neural network architectures, activation functions, and operations. It will present different loss functions and describe how training is performed via backpropagation. In the second part, the course will describe specific types of neural networks, e.g. convolutional, recurrent, and graph networks, as well as their applications in computer vision and natural language processing. The course will also briefly discuss reinforcement learning and unsupervised learning, in the context of neural networks. In addition to attending lectures and completing weekly/bi-weekly homework assignments, students will also carry out and present a project at the end of the course. The final grade will be based on homework (40%), exams (30%), project (25%) and participation (5%). Pre-requisites Math 220 (Calculus I) MATH 0280 Intro to Matrices and Linear Algebra CS 1501 (Algorithm Implementation)
Class No.: 29589 (1120)
Days: MW
Times: 3:00 pm - 4:15 pm
Room: IS 305
Instructor(s): S. Wood
TA(s):
Type: LEC
Session: AT
Writing:
Description: THE FUTURE OF CITIES. Prerequisite: CS/COE 0445

Anthony Townsend defines smart cities as "places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems." This rather expansive definition includes many past and contemporary projects with enormous investment and enthusiasm behind them, projects seeking to leverage an increasing interdependence on data-centric technologies and practices to change current and future cities. This course examines the historical dimensions of smart cities as both a collective imaginary and a contemporary reality from comparative, transnational and historical perspectives with ethics and values at its core. We will assess the politics and economics of data, the ethics of sensing and monitoring technologies, the environmental impacts of design, and the qualities of cities that make them livable and inclusive. This course affords the opportunity to critically analyze and disassemble the tools of the smart city. We will do this through employing various theoretical tools, research approaches and design methods.
Class No.: 26466 (1200)
Days: TuTh
Times: 8:00 am - 9:15 am
Room: IS 406
Instructor(s): W. Garrison III
TA(s):
Type: LEC
Session: AT
Writing:
Description: PRIVACY IN ELECTRONIC SOCIETY. Prerequisites: Completion of CS 0441, CS/COE 1501 and declared CS or COE major.

Privacy is an increasingly significant concern in our modern, connected society. We all share personal information on a daily basis with a wide range of organizations. Although at times such sharing can be intentional and beneficial for the user, other times information is shared against the user's will, used for purposes that the user did not expect, revealed to entities other than those approved by the user, or used to infer additional information that the user did not intend to reveal. In this course, students will learn to reason about what information is revealed through the use of computer systems. They will study several different scenarios in which information sharing is either unavoidable or (to some extent) desirable and discuss the balance between the benefits and costs of sharing. Finally, students will learn about several privacy enhancing technologies (PETs), and how these can be put to use by software developers to defend the privacy of their users.

Future Sections

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Past Sections

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