printer friendly pageCS 207: Antidiscrimination Law and Algorithmic Bias
Human decision making is increasingly being displaced by algorithms. Judges sentence defendants based on "risk scores;" regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. A predominant concern with the rise of such algorithmic decision making (machine learning or artificial intelligence) is that it may replicate or exacerbate human bias. Algorithms might discriminate, for instance, based on race or gender. This course surveys the legal principles for assessing bias of algorithms, examines emerging techniques for how to design and assess bias of algorithms, and assesses how antidiscrimination law and the design of algorithms may need to evolve to account for the potential emergence of machine bias. Admission is by consent of instructor and is limited to 20 students. Student assessment is based on class participation, response papers, and a final project. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (
https://law.stanford.edu/education/courses/consent-of-instructor-forms/). See Consent Application Form for instructions and submission deadline. Course same as
LAW 7073
Last offered: Autumn 2022
CS 208E: Great Ideas in Computer Science
Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material.
Last offered: Autumn 2021
CS 210A: Software Project Experience with Corporate Partners
Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS109 and
CS161.
Terms: Win
| Units: 3-4
CS 210B: Software Project Experience with Corporate Partners
Continuation of
CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites:
CS 210A
Terms: Spr
| Units: 3-4
Instructors:
Borenstein, J. (PI)
CS 212: Operating Systems and Systems Programming
Covers key concepts in computer systems through the lens of operatingsystem design and implementation. Topics include threads, scheduling,processes, virtual memory, synchronization, multi-core architectures,memory consistency, hardware atomics, memory allocators, linking, I/O,file systems, and virtual machines. Concepts are reinforced with fourkernel programming projects in the Pintos operating system. This classmay be taken as an accelerated single-class alternative to the
CS111,CS112 sequence; conversely, the class should not be taken by studentswho have already taken CS111 or
CS112.
Terms: Win
| Units: 3-5
Instructors:
Mazieres, D. (PI)
;
Baruah, N. (TA)
;
Bhak, N. (TA)
;
DeMarco, D. (TA)
;
Martinez-Piedra, G. (TA)
;
Nambi, S. (TA)
;
Park, J. (TA)
;
Yu, S. (TA)
CS 213: Creating Great VR: From Ideation to Monetization
Covering everything from VR fundamentals to futurecasting to launch management, this course will expose you to best practices and guidance from VR leaders that helps positions you to build great VR experiences.
Last offered: Spring 2018
CS 214: Selected Reading of Computer Science Research
Detailed reading of 5-10 research publications in computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to discuss the strengths and weaknesses of the work. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each piece of work includes a guest lecture by one of its authors.
Last offered: Spring 2022
CS 217: Hardware Accelerators for Machine Learning
This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. This course will cover classical ML algorithms such as linear regression and support vector machines as well as DNN models such as convolutional neural nets, and recurrent neural nets. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. We will cover the design of accelerators for ML model inference and training. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. To design energy-efficient accelerators, students will develop the intuition to make trade-offs between ML model parameters and hardware implementation techniques. Students will read recent research papers and complete a design project. Prerequisites:
CS 149 or
EE 180.
CS 229 is ideal, but not required.
Last offered: Winter 2023
CS 221: Artificial Intelligence: Principles and Techniques
Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites:
CS 103 or
CS 103B/X,
CS 106B or
CS 106X,
CS 109, and
CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Terms: Aut, Spr
| Units: 3-4
Instructors:
Anari, N. (PI)
;
Charikar, M. (PI)
;
Koyejo, S. (PI)
;
Liang, P. (PI)
;
Phatak, U. (PI)
;
Sadigh, D. (PI)
;
Agarwal, R. (TA)
;
Bruzzese, T. (TA)
;
Camacho, S. (TA)
;
Dixit, A. (TA)
;
Frausto, J. (TA)
;
Hejna, J. (TA)
;
Hu, G. (TA)
;
Jeon, H. (TA)
;
Kansal, A. (TA)
;
Khurana, A. (TA)
;
Ko, J. (TA)
;
Lee, A. (TA)
;
Li, C. (TA)
;
Lin, K. (TA)
;
Liu, S. (TA)
;
Melo, L. (TA)
;
Michaels, J. (TA)
;
Nie, N. (TA)
;
Pampari, A. (TA)
;
Pandya, D. (TA)
;
Roman, E. (TA)
;
Ryan, M. (TA)
;
Taori, R. (TA)
;
Tchapmi, M. (TA)
;
Tchapmi P., L. (TA)
;
Verma, S. (TA)
;
Wang, R. (TA)
;
Wornow, M. (TA)
;
Yang, S. (TA)
;
Zhang, P. (TA)
;
Zhang, T. (TA)
CS 223A: Introduction to Robotics (ME 320)
Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field. Recommended: matrix algebra.
Terms: Win
| Units: 3
Instructors:
Khatib, O. (PI)
;
Chong, W. (TA)
;
Devmalya, C. (TA)
...
more instructors for CS 223A »
Instructors:
Khatib, O. (PI)
;
Chong, W. (TA)
;
Devmalya, C. (TA)
;
Guo, W. (TA)
;
Kim, B. (TA)
;
Tupuri, S. (TA)
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