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41 - 50 of 99 results for: CS ; Currently searching spring courses. You can expand your search to include all quarters

CS 197C: Computer Science Research: CURIS Internship Onramp

A version of CS 197 designed specifically for students who will be participating in spring/summer CURIS internships OR have an ongoing research project with a (Ph.D. student or professor) mentor in the Stanford Computer Science department. An onramp for students interested in breaking new ground in the frontiers of computer science. Course format features faculty lectures introducing the fundamentals of computer science research, alongside mentorship and feedback from the CURIS or research mentor on a real research project. Students will attend the same lectures as CS197 and may enroll for 3 units. Lecture topics include reading technical papers, practicing oral communication and technical writing skills, and independently formulating research questions. Students must have commitment from their CURIS or research mentor for weekly check-in meetings. Prerequisite: CS106B.
Terms: Spr | Units: 3
Instructors: Miranda, B. (PI)

CS 198: Teaching Computer Science

Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://cs198.stanford.edu.
Terms: Aut, Win, Spr | Units: 3-4

CS 198B: Additional Topics in Teaching Computer Science

Students build on the teaching skills developed in CS198. Focus is on techniques used to teach topics covered in CS106B. Prerequisite: successful completion of CS198.
Terms: Aut, Win, Spr | Units: 1

CS 199: Independent Work

Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Adeli, E. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Bailis, P. (PI) ; Barrett, C. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Bohg, J. (PI) ; Boneh, D. (PI) ; Borenstein, J. (PI) ; Bouland, A. (PI) ; Boyd, S. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; Demszky, D. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fatahalian, K. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Finn, C. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Fox, E. (PI) ; Ganguli, S. (PI) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Gregg, C. (PI) ; Grimes, A. (PI) ; Guibas, L. (PI) ; Haber, N. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hennessy, J. (PI) ; Ho, D. (PI) ; Horowitz, M. (PI) ; Icard, T. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Khatib, O. (PI) ; Kjoelstad, F. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Lee, C. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Lin, H. (PI) ; Liu, K. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McKeown, N. (PI) ; Mirhoseini, A. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Niebles Duque, J. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Pande, V. (PI) ; Parlante, N. (PI) ; Patrignani, M. (PI) ; Pavone, M. (PI) ; Pea, R. (PI) ; Piech, C. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Potts, C. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Rubin, D. (PI) ; Rubinstein, A. (PI) ; Sadigh, D. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Stanford, J. (PI) ; Subramonyam, H. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wodtke, C. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yan, L. (PI) ; Yang, D. (PI) ; Yeung, S. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 199P: Independent Work

Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Achour, S. (PI) ; Agrawala, M. (PI) ; Aiken, A. (PI) ; Altman, R. (PI) ; Angst, R. (PI) ; Barrett, C. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (PI) ; Borenstein, J. (PI) ; Bouland, A. (PI) ; Brunskill, E. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Charikar, M. (PI) ; Cheriton, D. (PI) ; Dally, B. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Durumeric, Z. (PI) ; Engler, D. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Finn, C. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Fox, E. (PI) ; Genesereth, M. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Goodman, N. (PI) ; Grimes, A. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Hashimoto, T. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; James, D. (PI) ; Johari, R. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Khatib, O. (PI) ; Kochenderfer, M. (PI) ; Koller, D. (PI) ; Koyejo, S. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Lee, C. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Lin, H. (PI) ; Liu, K. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McKeown, N. (PI) ; Mirhoseini, A. (PI) ; Mitchell, J. (PI) ; Mitra, S. (PI) ; Musen, M. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Parlante, N. (PI) ; Pavone, M. (PI) ; Piech, C. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (PI) ; Reingold, O. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Savarese, S. (PI) ; Saxena, A. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Socher, R. (PI) ; Tan, L. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trippel, C. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Vitercik, E. (PI) ; Widom, J. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Wodtke, C. (PI) ; Wootters, M. (PI) ; Wu, J. (PI) ; Yamins, D. (PI) ; Yan, L. (PI) ; Yang, D. (PI) ; Young, P. (PI) ; Zaharia, M. (PI) ; Zelenski, J. (PI) ; Zou, J. (PI)

CS 202: Law for Computer Science Professionals

Businesses are built on ideas. Today's successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. Over the past 40 years, intellectual capital has emerged as the leading assets class. Ocean Tomo® estimates that over 80% of the market value of S&P 500 corporations now stems from intangible assets, which consist largely of intellectual property (IP) assets (e.g., the company and product names, logos and designs; patentable inventions; proprietary software and databases, and other proprietary product, manufacturing and marketing information). It is therefore vital for entrepreneurs and other business professionals to have a basic understanding of IP and how it is procured, protected, and exploited. This course provides an overview of the many and varied IP issues that students will confront during their careers. It is intended to be both informative and fun. Classes will cover the basics of patent, trademark, copyright, and trade secret law. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Emerging issues concerning the federal Computer Fraud & Abuse Act (CFAA) and hacking will be covered, as will employment issues, including employee proprietary information and invention assignment agreements, work made for hire agreements, confidentiality agreements, non-compete agreements and other potential post-employment restrictions. Recent notable lawsuits will be discussed, including Apple v. Samsung (patents), Alice Corp. v. CLS Bank (software and business method patents), Oracle v. Google (software/APIs), Waymo v. Uber (civil and criminal trade secret theft), and hiQ v. LinkedIn (CFAA). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Guest lectures typically include experts on open source software; legal and practical issues confronted by business founders; and, consulting and testifying as an expert in IP litigation. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise.
Terms: Spr | Units: 1
Instructors: Hansen, D. (PI)

CS 210B: Software Project Experience with Corporate Partners

Continuation of CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work. Prerequisites: CS 210A
Terms: Spr | Units: 3-4

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,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Terms: Aut, Spr | Units: 3-4

CS 224C: NLP for Computational Social Science

We live in an era where many aspects of our social interactions are recorded as textual data, from social media posts to medical and financial records. This course is about using a variety of techniques from machine learning and theories from social science to study human behaviors and important societal questions at scale. Topics will include methods for natural language processing and causal inference, and their applications to important societal questions around hate speech, misinformation, and social movements.
Terms: Spr | Units: 3
Instructors: Yang, D. (PI)

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284, SYMSYS 195N)

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Terms: Win, Spr | Units: 3-4
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