printer friendly pageCS 194: Software Project
Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeated for credit. Prerequisites: CS109 and
CS161.
Terms: Win, Spr
| Units: 3
| Repeatable
for credit
Instructors:
Borenstein, J. (PI)
;
Gawish, Z. (TA)
CS 194H: User Interface Design Project
Advanced methods for designing, prototyping, and evaluating user interfaces to computing applications. Novel interface technology, advanced interface design methods, and prototyping tools. Substantial, quarter-long course project that will be presented in a public presentation. Prerequisites:
CS 147, or permission of instructor.
Terms: Win
| Units: 3-4
Instructors:
Landay, J. (PI)
;
Zhou, G. (TA)
CS 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 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 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 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)
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Instructors:
Khatib, O. (PI)
;
Chong, W. (TA)
;
Devmalya, C. (TA)
;
Guo, W. (TA)
;
Kim, B. (TA)
;
Tupuri, S. (TA)
CS 225: Machine Learning for Discrete Optimization (MS&E 236)
Machine learning has become a powerful tool for discrete optimization. This is because, in practice, we often have ample data about the application domain?data that can be used to optimize algorithmic performance, ranging from runtime to solution quality. This course covers how machine learning can be used within the discrete optimization pipeline from many perspectives, including how to design novel combinatorial algorithms with machine-learned modules and configure existing algorithms? parameters to optimize performance. Topics will include both applied machinery (such as graph neural networks, reinforcement learning, transformers, and LLMs) as well as theoretical tools for providing provable guarantees.
Terms: Spr
| Units: 3
Instructors:
Vitercik, E. (PI)
;
Miraoui, Y. (TA)
CS 228: Probabilistic Graphical Models: Principles and Techniques
Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning. Prerequisites: basic probability theory and algorithm design and analysis.
Terms: Win
| Units: 3-4
Instructors:
Ermon, S. (PI)
;
Marx, C. (TA)
;
Patel, C. (TA)
;
Sharma, D. (TA)
;
Thomas, G. (TA)
;
Zalouk, S. (TA)
CS 229M: Machine Learning Theory (STATS 214)
How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering this question. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. Prerequisites: linear algebra (
MATH 51 or
CS 205), probability theory (
STATS 116,
MATH 151 or
CS 109), and machine learning (
CS 229,
STATS 229, or
STATS 315A).
Terms: Aut
| Units: 3
CS 241: Embedded Systems Workshop (EE 285)
Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of
CS107,
EE101A,
EE108,
ME80.
Terms: Win
| Units: 3
| Repeatable
3 times
(up to 9 units total)
Instructors:
Levis, P. (PI)
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