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How will digital health, low-cost patient-generated and genomic data enable precision medicine to transform health care? This Everyone Included¿ course from Stanford Medicine X and SHC Clinical Inference will provide an overview of data science principles and showcase real world solutions being created to advance precision medicine through implementation of digital health tools, machine learning and artificial intelligence approaches. This class will feature thought leaders and luminaries who are patients, technologists, providers, researchers and leading innovators from academia and industry. This course is open to undergraduate and graduate students. Lunch will be provided.
Terms: Aut
| Units: 1-2
| Repeatable
3 times
(up to 6 units total)
Instructors:
Chu, L. (PI)
;
Syed, Z. (PI)
ANTHRO 128A: The Boundaries of Humanity: Humans, Animals and Machines in the Age of Biotechnology
Advances in research and technology are blurring the boundaries between humans, animals, and machines, challenging conventional notions of human nature. Seminar explores the question of what it now means to be human and the personal, social, and ethical implications of our advancing technologies through the lens of various disciplines, including anthropology, cognitive psychology, neuroscience, genetics, evolutionary biology, biotechnology, and artificial intelligence. Includes guest speakers from fields and industries where important questions are being raised.
Last offered: Spring 2016
BIO 175: Collective Behavior and Distributed Intelligence (SYMSYS 275)
This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.
Terms: Spr
| Units: 3
CEE 329: Artificial Intelligence Applications in the AEC Industry
Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to participate actively in the lectures and discussions, submit triweekly reflection writings, and present their own evaluation of existing solutions. Enrollment limited to 12 students.
Terms: Spr
| Units: 2
Instructors:
Fischer, M. (PI)
;
Armeni, I. (TA)
CEE 329S: Seminar on Artificial Intelligence Applications in the AEC Industry
Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to actively prepare for and participate in all lectures and corresponding discussions.
Terms: Spr
| Units: 1
Instructors:
Fischer, M. (PI)
CS 21SI: AI for Social Good
Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. The class will focus on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of
CS107, mathematical fluency at the level of
CS103, comfort with probability at the level of
CS109 (or equivalent). Application required for enrollment.
Terms: Spr
| Units: 2
Instructors:
Piech, C. (PI)
CS 22A: The Social & Economic Impact of Artificial Intelligence (IPS 200)
Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Terms: Win
| Units: 1
Instructors:
Kaplan, J. (PI)
CS 54N: Great Ideas in Computer Science
Stanford Introductory Seminar. Preference to freshmen. Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. No prior experience with programming is assumed. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; and the philosophy behind artificial intelligence.
Last offered: Autumn 2016
| UG Reqs: GER:DB-EngrAppSci
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. Enrollment limited to students in the Master's program in Computer Science Education.
Terms: Aut
| Units: 3
Instructors:
Gregg, C. (PI)
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, Markov decision processes, graphical models, machine learning, and logic. Prerequisites:
CS 103 or
CS 103B/X,
CS 106B or
CS 106X,
CS 107, and
CS 109 (algorithms, probability, and programming experience).
Terms: Aut, Spr
| Units: 3-4
Instructors:
Ermon, S. (PI)
;
Liang, P. (PI)
;
Sadigh, D. (PI)
;
Baker, D. (TA)
;
Barnes, Z. (TA)
;
Beigi, B. (TA)
;
Bie, Z. (TA)
;
Blundell, H. (TA)
;
Despo, O. (TA)
;
Duan, T. (TA)
;
Eng, D. (TA)
;
Gabrielsson, R. (TA)
;
He, B. (TA)
;
Holmstrom, M. (TA)
;
Jiang, H. (TA)
;
Khandwala, N. (TA)
;
Kim, H. (TA)
;
Kurenkov, A. (TA)
;
Li, S. (TA)
;
Liu, D. (TA)
;
Mei, X. (TA)
;
Mussmann, S. (TA)
;
Peddada, A. (TA)
;
Pryzant, R. (TA)
;
Rege, A. (TA)
;
Sachidananda, V. (TA)
;
Sathya Moorthy, M. (TA)
;
Sauer, C. (TA)
;
Wang, A. (TA)
;
Wang, F. (TA)
;
Xing, M. (TA)
;
Yuan, Y. (TA)
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