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11 - 20 of 75 results for: artificial intelligence

CS 202: Law for Computer Science Professionals

An overview of intellectual property law as it relates to computer science and other disciplines, including discussions of patents, trademarks, copyrights, trade secrets, computer fraud litigation and interesting historical tidbits. Emphasis on topics of current interest such as software and business method patents, copyright issues concerning software, music, art and artificial intelligence, and current disputes of note including the recently-settled Waymo v. Uber lawsuit and the ongoing Oracle v. Google, Apple v. Samsung and hiQ v. LinkedIn sagas. Guest lectures typically have covered open source and the free software movement, practical issues for business founders (including corporate formation issues and non-disclosure, non-compete, work-made-for-hire and license agreements), and other pertinent topics. Classes are presented in an open discussion format broadly directed to students with both technical and non-technical backgrounds.
Terms: Aut | Units: 1
Instructors: Hansen, D. (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.
Terms: Aut | Units: 3

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, Sum | Units: 3-4

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

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/
Last offered: Winter 2018

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.
Last offered: Winter 2012 | Repeatable for 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.
| Repeatable for 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.
Last offered: Spring 2017

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

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.
Last offered: Spring 2018
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