COMPSCI 121 provides an introduction to problem solving and computer programming using the programming language Java. The course teaches how real-world problems can be solved computationally using the object-oriented metaphor that underlies Java. Concepts and techniques covered include data types, expressions, objects, methods, top-down program design, program testing and debugging, state representation, interactive programs, data abstraction, conditionals, iteration, interfaces, inheritance, polymorphism, arrays, graphics, and GUIs. No previous programming experience is required; however, this course is intended for Computer Science majors or those who plan on applying to the major. Non-majors are strongly encouraged to take one of our programming courses designed for non-majors. Use of a laptop computer on which you can install software is required. Prerequisite: R1 (or a score of 20 or higher on the math placement test Part A), or one of the following courses: MATH 101&102 or MATH 104 or MATH 127 or MATH 128 or MATH 131 or MATH 132. 4 credits.
The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to build and encapsulate data objects and their associated operations. Specific topics include linked structures, recursive structures and algorithms, binary trees, balanced trees, and hash tables. These topics are fundamental to programming and are essential to other courses in computer science. The course involves weekly programming assignments, in-class quizzes, discussion section exercises, and multiple exams. Prerequisites: COMPSCI 121 (or equivalent Java experience). A grade of B or better in COMPSCI 121 (or a grade of C or better in COMPSCI 186 (or COMPSCI 190D) is required for students enrolling in COMPSCI 187 and Basic Math Skills (R1). Basic Java language concepts are introduced quickly; if unsure of background, contact instructor. 4 credits.
This course provides a comprehensive introduction to the language of relational databases: Structured Query Language (SQL). Topics covered include: Entity-Relationship modeling, the Relational Model, the SQL language: data retrieval statements, data manipulation and data definition statements. All interactive reading problems involve the use of live SQL in the text InteractiveSQL (free). Homework problems will be done using databases running on software (SQLite) installed on student machines. This course is designed as a self-paced learning experience. The course is delivered in a series of assignments to provide a structure for students to work through the material. Students may work through the material at their own pace. All text and homework problems are available online. Instructor support is offered in an on-line format via chat sessions and forum postings. A final project will be developed and submitted by the end of the course. Software (free): SQLite- latest edition http://www.sqlite.org/download.html, DB Browser for SQLite http://sqlitebrowser.org/ Prerequisites: Basic computer literacy including ability to create and manipulate files and install software. 1 credit.
Through a careful analysis and discussion of a range of computing issues, topics, and polices, we will explore various impacts of computers on modern society. This class satisfies the Junior Year Writing requirement by providing directed practice and specific instruction in a range of writing genres. Students will produce approximately 20-25 pages of polished written work over the course of the semester. Prerequisite: ENGLWRIT 112 (or equivalent) and COMPSCI 220 and COMPSCI 230 and COMPSCI 240 (or COMPSCI 250). 3 credits.
Affective computing represents a broad, interdisciplinary research and practice area focusing on a range of topics, including: computational models of emotion, cognitive-affective architectures; affective user modeling; emotion sensing and recognition; emotion expression; and the use of emotions to improve human-computer interaction across a range of contexts including intelligent tutoring and gaming. This course will provide an introduction to affective computing through a combination of lectures, student presentations of selected literature, projects and class discussion. The course content and format will be appropriate for computer science, cognitive science, psychology, human factors, and industrial engineering students (advanced undergraduate / graduate). Prerequisite: Graduate or Senior level in Computer Science or Engineering, or permission of instructor. This course counts as a CS Elective toward the CS major (BA/BS). 3 credits.
Students will apply linear and non-linear (machine learning) analysis to program trace information to determine the predictability of computer performance in terms of measure such as cache miss rates. Background in machine learning is essential; background in computer performance is helpful. Permission of instructor is required. 3 credits.
Application of machine learning methods to modeling of ice melt and sea level rise. Requires background in machine learning techniques (they will not be taught) but not in climate science. Involves weekly research team meetings. Permission of instructor required. 3 credits.
This course examines examines the social norms, market factors, technology and law that influence Internet. Five content areas within this framework make up the scope of the course: Internet governance, communications, privacy, security, and intellectual property. Students will be expected to do graduate level reading and analysis, including primary case law (although there is no prerequisite to have taken a law course), contribute to a robust classroom discourse and write a substantial research paper focused on a topic within the scope. 3 credits
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