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21 - 30 of 76 results for: artificial intelligence

CS 247A: Design for Artificial Intelligence

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? Let¿s explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: 147 or equivalent background in design thinking.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: Stanford, J. (PI)

CS 257: Logic and Artificial Intelligence (PHIL 356C)

This is a course at the intersection of philosophical logic and artificial intelligence. After reviewing recent work in AI that has leveraged ideas from logic, we will slow down and study in more detail various components of high-level intelligence and the tools that have been designed to capture those components. Specific areas will include: reasoning about belief and action, causality and counterfactuals, legal and normative reasoning, natural language inference, and Turing-complete logical formalisms including (probabilistic) logic programming and lambda calculus. Our main concern will be understanding the logical tools themselves, including their formal properties and how they relate to other tools such as probability and statistics. At the end, students should expect to have learned a lot more about logic, and also to have a sense for how logic has been and can be used in AI applications. Prerequisites: A background in logic, at least at the level of Phil 151, will be expected. In case a student is willing to put in the extra work to catch up, it may be possible to take the course with background equivalent to Phil 150 or CS 157. A background in AI, at the level of CS 221, would also be very helpful and will at times be expected. 2 unit option only for PhD students past the second year. Course website: http://web.stanford.edu/class/cs257/
Terms: not given this year, last offered Winter 2018 | Units: 2-4 | Grading: Letter or Credit/No Credit

CS 294A: Research Project in Artificial Intelligence

Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.
Terms: not given this year, last offered Winter 2012 | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 329: Topics in Artificial Intelligence

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 329M: Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning

This advanced graduate course explores in depth several important classes of algorithms in modern machine learning. We will focus on understanding the mathematical properties of these algorithms in order to gain deeper insights on when and why they perform well. We will also study applications of each algorithm on interesting, real-world settings. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Students will learn mathematical techniques for analyzing these algorithms and hands-on experience in using them. We will supplement the lectures with latest papers and there will be a significant research project component to the class. Prerequisites: Probability ( CS 109), linear algebra ( Math 113), machine learning ( CS 229), and some coding experience.
Terms: not given this year, last offered Spring 2017 | Units: 3 | Grading: Letter or Credit/No Credit

CS 421: Designing AI to Cultivate Human Well-Being

Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating human well-being. The goal of this class is to bridge the gap between technology and societal objectives: How do we design AI to promote human flourishing? On Day 1, we draw on behavioral research to discuss what makes humans thrive. Behavioral research shows that for people to flourish, they need meaning, which involves an ability to understand and value others, a sense of belonging, and knowledge that they are making a contribution bigger than themselves. The conditions for this occur when people feel they have the resources and insight to establish a sense of meaning for themselves. Students will draw on this research to focus on building AI technology that effectively understands, communicates with, collaborates with and augment people. On days 2-5, leaders across industries (e.g., healthcare, transportation) that fundamentally affect human wellbeing will participate in lightning round exchanges to delve deeply into the challenge of building technology focused on human well-being, followed by interactive discussion with students. On the last day, the four-person cross-disciplinary teams will present their 2 page white paper proposals to invited guests. Of note: this course is entirely about high-level "programming" and provides no technical insight on machine learning, data-mining or statistical pattern recognition.
Terms: Win | Units: 2 | Grading: Satisfactory/No Credit

CS 521: Seminar on AI Safety

In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. We will explore some of the major problems in this area from the viewpoint of industry and academia. We plan to have a weekly seminar speaker to discuss issues such as verification of AI systems, reward misalignment and hacking, secure and attack-resilient AI systems, diagnosis and repair, issues regarding policy and ethics, as well as the implications of AI safety in automotive industry. Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended.
Terms: not given this year, last offered Spring 2018 | Units: 1 | Grading: Satisfactory/No Credit

CS 522: Seminar in Artificial Intelligence in Healthcare

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at shift.stanford.edu/healthai.
Terms: not given this year, last offered Autumn 2018 | Units: 1 | Grading: Satisfactory/No Credit

ENGLISH 13Q: Imaginative Realms

This class looks at the tradition of the imagined universe in fiction and poetry. Special topics include magical realism, artificial intelligence, and dystopias. Primary focus on giving students a skill set to tap into their own creativity. Opportunities for students to explore their creative strengths, develop a vocabulary with which to discuss their own creativity, and experiment with the craft and adventure of their own writing. For undergrads only.
Terms: not given this year, last offered Autumn 2018 | Units: 3 | UG Reqs: WAY-CE | Grading: Letter (ABCD/NP)

ENGLISH 106: A.I.: Artificial Intelligence in Fiction (AMSTUD 106A)

From self-driving cars to bots that alter democratic elections, artificial intelligence is growing increasingly powerful and prevalent in our everyday lives. Literature has long been speculating about the techno-utopia¿and catastrophe¿that A.I. could usher in. Indeed, literature itself presents us with a kind of A.I. in the many characters that speak and think in its pages. But how do we classify an intelligence as ¿artificial¿ or not? Is there a clear boundary that demarcates bodies from machines? What, if anything, separates the ¿genre¿ of technology from that of literature? What classifies literature as ¿science fiction,¿ ¿scientific,¿ ¿futuristic,¿ ¿psychological,¿ or ¿dystopian¿? And can technology or literature ever overcome the ultimate division between all intelligences¿the problem of other minds? This course consists in curated multi-genre combinations of literature, philosophy, film, and television that explore what makes someone¿or something¿a person in our world today. Special events will include celebrating the current bicentennial of Mary Shelley¿s Frankenstein (1818) in Stanford Special Collections; a possible visit to Stanford¿s A.I. Laboratory; and chatting with the ELIZA chatbot.
Terms: not given this year, last offered Autumn 2018 | Units: 5 | Grading: Letter (ABCD/NP)
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