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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
Instructors:
Davies, T. (PI)
;
Gordon, D. (PI)
CME 238: Artificial Intelligence in Financial Technology (MS&E 446)
Survey the current Financial Technology landscape through the lens of Artificial Intelligence applications, with emphasis in 4 areas: Payments, Blockchain and Cryptocurrencies, Robo-Advisory, and Marketplace Lending. Students work in groups of 4 to develop an original financial technology project, research paper or product prototype within a chosen area. Final project posters to be presented to the class and posted online. Top posters to be selected and presented at the Stanford Financial Technology conference in January. Classes will alternate between industry speakers, lectures and scheduled group meetings with teaching team. Advanced undergraduates, graduate students, and students from other Schools are welcome to enroll. Prerequisites: Basic programming skills, knowledge of design process, introductory statistics. No formal finance experience required. Enrollment is capped at 32.
Terms: Aut
| Units: 3
| Repeatable
6 times
(up to 18 units total)
CS 22A: The Social & Economic Impact of Artificial Intelligence
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: Spr
| 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.
Terms: Aut
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Roberts, E. (PI)
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:
Roberts, E. (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
| Units: 3-4
Instructors:
Liang, P. (PI)
;
Abdulhamid, A. (TA)
;
Agrawal, A. (TA)
...
more instructors for CS 221 »
Instructors:
Liang, P. (PI)
;
Abdulhamid, A. (TA)
;
Agrawal, A. (TA)
;
Anenberg, B. (TA)
;
Bhatnagar, P. (TA)
;
Cai, Y. (TA)
;
Caswell, I. (TA)
;
Chen, M. (TA)
;
Dasu, G. (TA)
;
Dong, C. (TA)
;
Goel, K. (TA)
;
Han, A. (TA)
;
Hsu, I. (TA)
;
Khandwala, N. (TA)
;
Kim, H. (TA)
;
LaRow, W. (TA)
;
Lee, T. (TA)
;
Moore, D. (TA)
;
Musa, R. (TA)
;
Rai, K. (TA)
;
Shi, T. (TA)
;
Sohmshetty, A. (TA)
;
Wang, L. (TA)
;
Wang, Y. (TA)
;
Wu, K. (TA)
;
Yan, K. (TA)
CS 227B: General Game Playing
A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr
| Units: 3
Instructors:
Genesereth, M. (PI)
CS 257: Logic and Artificial Intelligence (PHIL 356C)
This is a course at the intersection of philosophical logic and artificial intelligence. The goal of the course is to understand the role that expressive logical frameworks might play in AI, and to gain a deeper understanding of how different logical systems relate, and what features of a logical system could make it useful for representation and/or reasoning. Specific themes may include: 1. Tradeoff between complexity and expressivity 2. Capturing subtle reasoning about agent mental states 3. Defeasibility, causality, and the relation between logic and probability 4. Logical formalizations of legal and normative reasoning 5. Combining statistical learning and inference with rich logical structure 6. Logical systems close to the structure of natural language ("natural logics"). Prerequisites: It is expected that students already have a solid background in logic.
Phil 151 is ideal, but
Phil 150 or
CS 157 would be acceptable, with the understanding that there may be some catching up to do. 2 unit option for PhD students only.
Terms: Win
| Units: 2-4
Instructors:
Icard, T. (PI)
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