printer friendly pageCS 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 177: Human Centered Product Management
Ask any product person what the most important skills are for PMs and they'll say interpersonal dynamics-- negotiation, communication, conflict resolution, interviewing and more. This class will look at the role of product management through a human-centered lens, including customers and coworkers. As well, students will experience the Agile-Lean-UX development process. Course enrollment will be capped, an application will be sent out first day of class. Prerequisite:
CS106A&B or equivalent. This class could be taken before or after 147.
Terms: Aut
| Units: 3-4
CS 181: Computers, Ethics, and Public Policy
Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work. Prerequisite:
CS106A. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see
https://cs181.stanford.edu for more information.
Terms: Spr
| Units: 4
| UG Reqs: WAY-ER, GER:EC-EthicReas
Instructors:
Gregg, C. (PI)
;
Winstein, K. (PI)
CS 182: Ethics, Public Policy, and Technological Change (COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182)
Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite:
CS106A.
Last offered: Winter 2023
| UG Reqs: WAY-ER
CS 182W: Ethics, Public Policy, and Technological Change (WIM)
Writing-intensive version of
CS182. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, Math/Comp Sci, and Data Science undergraduates (and is only open to those majors). Prerequisite:
CS106A. See CS182 for lecture day/time information. Enroll in either
CS 182 or
CS 182W,not both. Enrollment in WIM version of the course is limited to 125students. Enrollment is restricted to seniors and coterminal students until January 9, 2023. Starting January 9, 2023, enrollment will open to all students if additional spaces remain available in the class.
Last offered: Winter 2023
| UG Reqs: WAY-ER
CS 193C: Client-Side Internet Technologies
Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax. Prerequisite: programming experience at the level of
CS106A.
Terms: Sum
| Units: 3
Instructors:
Young, P. (PI)
CS 193P: iOS Application Development
Build mobile applications using tools and APIs in iOS. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. This course will require you to learn a new programming language (Swift) as well as the iOS development environment, SwiftUI. Prerequisites: All coursework (homework and final project) involves writing code, so writing a lot of code should not be new to you (coding experience in almost any language is valuable, but object-oriented (e.g.
CS108) and/or functional programming languages (e.g.
CS43) are most highly recommended). CS106A and B (or X) and
CS107 (or equivalent) are hard prerequisites. Any other courses that help to develop your maturity as a programmer are also recommended.
Last offered: Spring 2023
CS 229: Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.
Terms: Aut, Win, Sum
| Units: 3-4
Instructors:
Amjad, J. (PI)
;
Charikar, M. (PI)
;
Fox, E. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
;
Ng, A. (PI)
;
Agarwal, R. (TA)
;
Agarwala, S. (TA)
;
Chang, C. (TA)
;
Chi, R. (TA)
;
Chow, W. (TA)
;
Chu, S. (TA)
;
Damiani, A. (TA)
;
Deng, R. (TA)
;
Desai, R. (TA)
;
Ding, Z. (TA)
;
Dong, K. (TA)
;
Frausto, J. (TA)
;
Jeon, H. (TA)
;
Khandelwal, P. (TA)
;
Kumbong, H. (TA)
;
Schaeffer, R. (TA)
;
So, J. (TA)
;
Wang, A. (TA)
;
Wang, R. (TA)
;
Xiao, Z. (TA)
;
Yang, S. (TA)
;
Zhang, E. (TA)
CS 237B: Principles of Robot Autonomy II (AA 174B, AA 274B, EE 260B)
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.
Terms: Win
| Units: 3-4
Instructors:
Bohg, J. (PI)
;
Pavone, M. (PI)
;
Sadigh, D. (PI)
;
Agarwal, T. (TA)
;
Chen, C. (TA)
;
Manhas, A. (TA)
CS 270: Modeling Biomedical Systems (BIOMEDIN 210)
At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites:
CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win, Spr
| Units: 3
Instructors:
Musen, M. (PI)
Filter Results: