printer friendly pageCS 108: Object-Oriented Systems Design
Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams. Prerequisite: 107.
Terms: Aut, Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Young, P. (PI)
;
Do, K. (TA)
;
Igbinedion, I. (TA)
;
Marks, M. (TA)
;
Short, L. (TA)
;
Truong, K. (TA)
;
Whittington, Z. (TA)
CS 109: Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of
MATH 51 or
CME 100 or equivalent.
Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors:
Piech, C. (PI)
;
Sahami, M. (PI)
;
Agrawal, A. (TA)
;
Apostolatos, A. (TA)
;
Chen, M. (TA)
;
Eric, M. (TA)
;
Goodman, I. (TA)
;
Haaland, C. (TA)
;
Hsu, I. (TA)
;
Lin, P. (TA)
;
Luo, A. (TA)
;
Martinez, P. (TA)
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Nigam, P. (TA)
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Puranik, A. (TA)
;
Rao, I. (TA)
;
Salloum, J. (TA)
;
Shapiro, J. (TA)
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Sun, Y. (TA)
;
Wang, D. (TA)
;
Weiler, A. (TA)
CS 109L: Statistical Computing with R Laboratory
Supplemental lab to
CS109. Introduces the R programming language for statistical computing. Topics include basic facilities of R including mathematical, graphical, and probability functions, building simulations, introductory data fitting and machine learning. Provides exposure to the functional programming paradigm. Corequisite:
CS109.
Last offered: Spring 2015
CS 110: Principles of Computer Systems
Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; security, and encryption; and performance optimizations. Prerequisite: 107.
Terms: Aut, Win, Spr
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci
CS 123: Programming Your Personal Robot
An introduction to the programming of a sensor-rich personal robot. This course extends programming from the virtual environment into the physical world, which presents unique challenges. Focus is on three areas of intellectual discourse that are fundamental to the programming of physical devices: communication with the devices; programming of event driven behaviors; and reasoning with uncertainty. The concepts introduced will be put into practical use through a series of class projects centered around programming your personal robot. This course also serves as a good introduction to Experimental Robotics by exposing students to basic concepts and techniques that are relevant for real world robot programming. Prerequisite: Basic knowledge of computer programming (as covered in
CS 106). Knowledge of Python is recommended.
Terms: Aut
| Units: 3
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, genome sequences, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, machine translation, genomic sequence alignment, spell checking, speech processing, recommender systems. Prerequisite:
CS103,
CS107,
CS109.
Terms: Win
| Units: 3-4
Instructors:
Jurafsky, D. (PI)
;
Garcia, B. (TA)
;
Grace, M. (TA)
;
Gupta, R. (TA)
;
Huang, B. (TA)
;
Lu, J. (TA)
;
McKenzie, K. (TA)
;
Silveira, N. (TA)
;
arivazhagan, N. (TA)
CS 131: Computer Vision: Foundations and Applications
Robots that can navigate space and perform duties, search engines that can index billions of images and videos, algorithms that can diagnose medical images for diseases, or smart cars that can see and drive safely: Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of computer vision. Course will introduce a number of fundamental concepts in computer vision and expose students to a number of real-world applications, plus guide students through a series of well designed projects such that they will get to implement cutting-edge computer vision algorithms. Prerequisites: Students should be familiar with Matlab (i.e. have programmed in Matlab before) and Linux; plus Calculus & Linear Algebra.
Terms: Aut
| Units: 3-4
CS 140: Operating Systems and Systems Programming
Operating systems design and implementation. Basic structure; synchronization and communication mechanisms; implementation of processes, process management, scheduling, and protection; memory organization and management, including virtual memory; I/O device management, secondary storage, and file systems. Prerequisite:
CS 110.
Terms: Aut, Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Mazieres, D. (PI)
;
Ousterhout, J. (PI)
;
Eby, C. (TA)
...
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Instructors:
Mazieres, D. (PI)
;
Ousterhout, J. (PI)
;
Eby, C. (TA)
;
Lee, C. (TA)
;
Mani, A. (TA)
;
Moody, R. (TA)
;
Plattner, K. (TA)
;
Qin, H. (TA)
;
Washington, P. (TA)
;
Zhu, C. (TA)
;
Zhu, N. (TA)
CS 142: Web Applications
Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, javascript, and the document object model. Server-side technologies such as sessions, templates, relational databases, and object-relational mapping. Issues in web security and application scalability. New models of web application deployment. Prerequisites:
CS 107 and
CS 108.
Terms: Win, Spr
| Units: 3
Instructors:
Rosenblum, M. (PI)
;
Du, Y. (TA)
;
Garcia, B. (TA)
;
Han, K. (TA)
;
Kao, S. (TA)
;
Leishman, A. (TA)
;
Lu, J. (TA)
;
Luong, R. (TA)
;
Mai, D. (TA)
;
Seira Silva-Herzog, S. (TA)
;
Shin, K. (TA)
;
Tan, J. (TA)
CS 143: Compilers
Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects. Prerequisites: 103 or 103B, and 107.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Aiken, A. (PI)
;
Clapp Jimenez Labora, L. (TA)
;
Do, K. (TA)
...
more instructors for CS 143 »
Instructors:
Aiken, A. (PI)
;
Clapp Jimenez Labora, L. (TA)
;
Do, K. (TA)
;
Heule, S. (TA)
;
Papadakis, M. (TA)
;
Srinivasan, S. (TA)
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