printer friendly pageCS 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)
;
Ng, A. (PI)
;
Doby, S. (TA)
...
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Instructors:
Bensouda Mourri, Y. (PI)
;
Ng, A. (PI)
;
Doby, S. (TA)
;
Kaputa, Z. (TA)
;
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)
;
Niebles Duque, J. (PI)
;
Chang, J. (TA)
...
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Instructors:
Gaidon, A. (PI)
;
Niebles Duque, J. (PI)
;
Chang, J. (TA)
;
Chen, H. (TA)
;
Chou, A. (TA)
;
Moore, A. (TA)
;
Qu, C. (TA)
;
Tchapmi, M. (TA)
CS 137A: Principles of Robot Autonomy I (AA 174A, EE 160A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3-4
CS 139: Human-Centered AI
Artificial Intelligence technology can and must be guided by human concerns. The course examines how mental models and user models of AI systems are formed, and how that leads to user expectations. This informs a set of design guidelines for building AI systems that are trustworthy, understandable, fair, and beneficial. The course covers the impact of AI systems on the economy and everyday life, and ethical issues of collecting data and running systems, including respect for persons, beneficence, fairness and justice.
Terms: Spr
| Units: 3
Instructors:
Norvig, P. (PI)
;
Russell, D. (PI)
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 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, 107 equivalent, or consent from instructor.
Terms: Spr
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Kjoelstad, F. (PI)
;
Dong, J. (TA)
;
Gale, T. (TA)
;
Hsu, O. (TA)
;
Rebelsky, D. (TA)
;
Schultz, C. (TA)
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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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)
;
Husman, G. (TA)
;
Kim, J. (TA)
;
Kulkarni, T. (TA)
;
Miller, G. (TA)
;
Mitchell, Y. (TA)
;
Poole, R. (TA)
;
Uhegbu, K. (TA)
;
Wang, R. (TA)
CS 145: Data Management and Data Systems
Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning. Prerequisites: 103 and 107 (or equivalent).
Terms: Aut
| Units: 3-4
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Shivakumar, N. (PI)
;
Badrinath, A. (TA)
;
Bradfield, A. (TA)
...
more instructors for CS 145 »
Instructors:
Shivakumar, N. (PI)
;
Badrinath, A. (TA)
;
Bradfield, A. (TA)
;
Chang, J. (TA)
;
Escandon, E. (TA)
;
Li, R. (TA)
;
Maheshwari, E. (TA)
;
Wu, J. (TA)
CS 147: Introduction to Human-Computer Interaction Design
Introduces fundamental methods and principles for designing, implementing, and evaluating user interfaces. Topics: user-centered design, rapid prototyping, experimentation, direct manipulation, cognitive principles, visual design, social software, software tools. Learn by doing: work with a team on a quarter-long design project, supported by lectures, readings, and studios. Prerequisite: 106B or X or equivalent programming experience. Recommended that CS Majors have also taken one of 142, 193P, or 193A.nnPlease note: Less than 5 is only allowed for graduate students.
Terms: Aut
| Units: 3-5
Instructors:
Landay, J. (PI)
;
Doby, S. (TA)
;
Hoang, N. (TA)
;
Joerke, M. (TA)
;
Lee, J. (TA)
;
Lee, T. (TA)
;
Leon, A. (TA)
;
Zhou, G. (TA)
CS 147L: Cross-platform Mobile App Development
The fundamentals of cross-platform mobile application development with a focus on the React Native framework (RN). Primary focus on developing best practices in creating apps for both iOS and Android by using Javascript and existing web + mobile development paradigms. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Airbnb, Walmart, Tesla, and UberEats. Skills developed over the course will be consolidated by the completion of a final project. Required Prerequisites:
CS106B.
Terms: Aut
| Units: 3
Instructors:
Cheng, A. (PI)
;
Landay, J. (PI)
;
Kacharia, N. (TA)
...
more instructors for CS 147L »
Instructors:
Cheng, A. (PI)
;
Landay, J. (PI)
;
Kacharia, N. (TA)
;
Lu, P. (TA)
;
Poole, R. (TA)
;
Wan Rosli, D. (TA)
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