CS 111: Operating Systems Principles
Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS110 that fulfills any requirement satisfied by
CS110. Prerequisite:
CS107.
Terms: Aut, Win, Spr
| Units: 3-5
Instructors:
Ousterhout, J. (PI)
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Troccoli, N. (PI)
;
Ahmad-Stein, D. (TA)
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more instructors for CS 111 »
Instructors:
Ousterhout, J. (PI)
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Troccoli, N. (PI)
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Ahmad-Stein, D. (TA)
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Ayoob, M. (TA)
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Baruah, N. (TA)
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Cao, M. (TA)
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Dange, R. (TA)
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Escandon, E. (TA)
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Gorelik, I. (TA)
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Govil, Y. (TA)
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Granado, M. (TA)
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Hanlon, M. (TA)
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Kansal, A. (TA)
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Khandelwal, P. (TA)
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Kohli, S. (TA)
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Li, G. (TA)
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Marchini, M. (TA)
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Palleti, R. (TA)
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Saracay, E. (TA)
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Tang, E. (TA)
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Verma, S. (TA)
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Zhang, X. (TA)
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Zuna Largo, W. (TA)
CS 111ACE: Problem Solving Lab for CS111
Additional design and implementation problems to complement the material taught in
CS111. In-class participation is required. Prerequisite: consent of instructor. Corequisite:
CS111
Terms: Aut, Win, Spr
| Units: 1
Instructors:
Master, T. (PI)
CS 112: Operating systems kernel implementation project
Students will learn the details of how operating systems work throughfour implementation projects in the Pintos operating system. Theprojects center around threads, processes, virtual memory, and filesystems. This class should not be taken by students who have taken orplan to take CS212 or
CS140. Prerequisite: CS111 or permission of theinstructor.
Terms: Win
| Units: 3
Instructors:
Mazieres, D. (PI)
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Baruah, N. (TA)
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Bhak, N. (TA)
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DeMarco, D. (TA)
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Martinez-Piedra, G. (TA)
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Nambi, S. (TA)
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Park, J. (TA)
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Yu, S. (TA)
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS106B, Python (at the level of
CS106A),
CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to
CS107 (or taking CS107 or CS1U concurrently).
Terms: Win
| Units: 3-4
| UG Reqs: WAY-AQR
CS 129: Applied Machine Learning
(Previously numbered
CS 229A.) You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as
Math 51.
Terms: Win
| Units: 3-4
Instructors:
Bensouda Mourri, Y. (PI)
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Ng, A. (PI)
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Doby, S. (TA)
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more instructors for CS 129 »
Instructors:
Bensouda Mourri, Y. (PI)
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Ng, A. (PI)
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Doby, S. (TA)
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Kaputa, Z. (TA)
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Marklund, H. (TA)
CS 131: Computer Vision: Foundations and Applications
Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars 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. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.
Terms: Win
| Units: 3-4
Instructors:
Gaidon, A. (PI)
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Niebles Duque, J. (PI)
;
Chang, J. (TA)
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more instructors for CS 131 »
Instructors:
Gaidon, A. (PI)
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Niebles Duque, J. (PI)
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Chang, J. (TA)
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Chen, H. (TA)
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Chou, A. (TA)
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Moore, A. (TA)
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Qu, C. (TA)
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Tchapmi, M. (TA)
CS 140E: Operating systems design and implementation
Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.
Terms: Win
| Units: 3-4
CS 144: Introduction to Computer Networking
Principles and practice. Structure and components of computer networks, with focus on the Internet. Packet switching, layering, and routing. Transport and TCP: reliable delivery over an unreliable network, flow control, congestion control. Network names, addresses and ethernet switching. Includes significant programming component in C/C++; students build portions of the internet TCP/IP software. Prerequisite:
CS110.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Winstein, K. (PI)
;
Ahmed, K. (TA)
;
Cheruiyot, I. (TA)
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more instructors for CS 144 »
Instructors:
Winstein, K. (PI)
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Ahmed, K. (TA)
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Cheruiyot, I. (TA)
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Hanlon, M. (TA)
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Husman, G. (TA)
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Kim, J. (TA)
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Kulkarni, T. (TA)
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Miller, G. (TA)
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Mitchell, Y. (TA)
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Poole, R. (TA)
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Uhegbu, K. (TA)
;
Wang, R. (TA)
CS 153: Applied Security at Scale
This course is designed to help students understand the unique challenges of solving security problems at scale, and is taught by senior technology leaders from companies tackling hardware and software security for hundreds of millions of people. The course is split into six parts covering major themes: Basics, Confidential Computing, Privacy, Trust, Safety and Real World. The format of the class will include guest lectures from experts in each theme, covering a blend of both theory and real world scenarios. Prerequisite:
CS110/
CS111. Recommended but not required:
CS155.
Terms: Win, Spr
| Units: 3
Instructors:
Abbott, M. (PI)
CS 161: Design and Analysis of Algorithms
Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 106B or 106X; 103 or 103B; 109 or
STATS 116.
Terms: Aut, Win, Sum
| Units: 3-5
| UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors:
Anari, N. (PI)
;
Charikar, M. (PI)
;
Rubinstein, A. (PI)
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more instructors for CS 161 »
Instructors:
Anari, N. (PI)
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Charikar, M. (PI)
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Rubinstein, A. (PI)
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Conkey, A. (TA)
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Cussen, H. (TA)
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Dixit, A. (TA)
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Garg, R. (TA)
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Goyal, S. (TA)
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Hosgur, E. (TA)
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Ko, J. (TA)
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Kolichala, P. (TA)
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Li, W. (TA)
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Liu, S. (TA)
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Mayer, T. (TA)
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Namboothiry, B. (TA)
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Parada, R. (TA)
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Patterson, K. (TA)
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Rivkin, J. (TA)
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Roghani, M. (TA)
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Salahi, K. (TA)
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Singh, A. (TA)
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Singhal, A. (TA)
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Stearns, C. (TA)
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Wang, X. (TA)
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Z. HaoChen, J. (TA)
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Zhang, P. (TA)
;
Zhao, J. (TA)
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