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CS 10SC: Great Ideas in Computer Science

Computers have come to permeate many aspects of our lives, from how we communicate with each other to how we produce and consume information. And while it is all too easy to think of computing in terms of the products and applications we see emerging from technology companies, the intellectual foundations of computer science go much deeper. Indeed, beneath the surface of the tools we use, the social networks we engage in, and the web of information we search, lays a field rich with fascinating, intellectually exciting, and sometimes unexpectedly surprising ideas.nnIn this seminar, we will explore several of the great ideas in computer science, looking at both challenging problems and their impact on real applications. From understanding how search engines on the Web work to looking at mathematical theories underlying social networks, from questioning whether a computer can be intelligent to analyzing the notion of what is even possible to compute, this seminar will take us on a series of intellectual excursions that will change the way you look at computers.nnNo prior experience with computer science or programming is required, but a high school mathematics background, an interest in problem-solving, and a healthy curiosity will go a long way toward ensuring an enjoyable and enlightening experience. Students will work in small groups to research topics in computer science they find most intriguing. The course will also take advantage of Stanford's location in the heart of Silicon Valley by conducting field trips to a local company and the Computer History Museum. Sophomore College Course: Application required, due noon, April 7, 2015. Apply at http://soco.stanford.edu
Terms: Sum | Units: 2
Instructors: Roberts, E. (PI)

CS 12SC: Computational Decision Making

Although we make decisions every day, many people base their decisions on initial reactions or ¿gut¿ feelings. There are, however, powerful frameworks for making decisions more effectively based on computationally analyzing the choices available and their possible outcomes. In this course we give an introduction to some of these frameworks, including utility theory, decision analysis, game theory, and Markov decision processes. We also discuss why people sometimes make seemingly reasonable, yet irrational, decisions. We begin the class by presenting some of the basics of probability theory, which serves as the main mathematical foundation for the decision making frameworks we will subsequently present. Although we provide a mathematical/computational basis for the decision making frameworks we examine, we also seek to give intuitive (and sometime counterintuitive) explanations for actual decision making behavior through in-class demonstrations. No prior experience with probability theory is needed (we¿ll cover what you need to know in class), but students should be comfortable with mathematical manipulation at the level of Math 41. Sophomore College Course: Application required, due noon, April 7, 2015. Apply at http://soco.stanford.edu
Terms: Sum | Units: 2
Instructors: Sahami, M. (PI)

CS 106A: Programming Methodology (ENGR 70A)

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Uses the Java programming language. Emphasis is on good programming style and the built-in facilities of the Java language. No prior programming experience required. Summer quarter enrollment is limited. Priority given to Stanford students.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: WAY-FR, GER:DB-EngrAppSci

CS 106B: Programming Abstractions (ENGR 70B)

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent. Summer quarter enrollment is limited. Priority given to Stanford students.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Roberts, E. (PI) ; Stepp, M. (PI) ; Watson, R. (PI)

CS 109: Introduction to Probability for Computer Scientists

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: WAY-AQR, WAY-FR, GER:DB-EngrAppSci
Instructors: Dror, R. (PI) ; Le, E. (PI) ; Lee, C. (PI) ; Shin, K. (PI)

CS 148: Introduction to Computer Graphics and Imaging

Introductory prerequisite course in the computer graphics sequence introducing students to the technical concepts behind creating synthetic computer generated images. Focuses on using OpenGL to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Course will cover fundamental understanding of light and color, as well as how it impacts computer displays and printers. Class will discuss more thoroughly how light interacts with the environment, constructing engineering models such as the BRDF, plus various simplifications into more basic lighting and shading models. Also covers ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class mini-project consists of building out a ray tracer to create visually compelling images. Starter codes and code bits will be provided to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted toward in person "demos" of the code in action - creativity and the production of impressive visual imagery are highly encouraged. Prerequisites: CS 107, MATH 51.
Terms: Aut, Sum | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-CE
Instructors: Fedkiw, R. (PI) ; Niessner, M. (PI) ; Savva, M. (PI)

CS 161: Design and Analysis of Algorithms

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.
Terms: Aut, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-FR
Instructors: Lee, H. (PI) ; Plotkin, S. (PI) ; Williams, V. (PI)

CS 191: Senior Project

Restricted to Computer Science and Computer Systems Engineering students. Group or individual projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include substantial programming and modern user-interface technologies and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Required public presentation of final application or research results.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable for credit
Instructors: Aiken, A. (PI) ; Akeley, K. (PI) ; Altman, R. (PI) ; Angst, R. (PI) ; Baker, M. (PI) ; Barbagli, F. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Blikstein, P. (PI) ; Boneh, D. (PI) ; Boyd, S. (PI) ; Bradski, G. (PI) ; Brafman, R. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Casado, M. (PI) ; Cheriton, D. (PI) ; Cooper, S. (PI) ; Dally, B. (PI) ; De-Micheli, G. (PI) ; Dill, D. (PI) ; Dror, R. (PI) ; Dwork, C. (PI) ; Engler, D. (PI) ; Ermon, S. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Golub, G. (PI) ; Goodman, N. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Heer, J. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Johari, R. (PI) ; Johnson, M. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Klemmer, S. (PI) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (PI) ; Kozyrakis, C. (PI) ; Kundaje, A. (PI) ; Lam, M. (PI) ; Landay, J. (PI) ; Latombe, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Liang, P. (PI) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (PI) ; Montanari, A. (PI) ; Motwani, R. (PI) ; Musen, M. (PI) ; Nass, C. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Nilsson, N. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Paepcke, A. (PI) ; Pande, V. (PI) ; Parlante, N. (PI) ; Pea, R. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Re, C. (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) ; Stepp, M. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Valiant, G. (PI) ; Van Roy, B. (PI) ; Wang, G. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Williams, V. (PI) ; Winograd, T. (PI) ; Winstein, K. (PI) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 192: Programming Service Project

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value.
Terms: Aut, Win, Spr, Sum | Units: 1-4 | Repeatable for credit
Instructors: Aiken, A. (PI) ; Altman, R. (PI) ; Baker, M. (PI) ; Barbagli, F. (PI) ; Batzoglou, S. (PI) ; Bejerano, G. (PI) ; Bernstein, M. (PI) ; Boneh, D. (PI) ; Bradski, G. (PI) ; Brafman, R. (PI) ; Cain, J. (PI) ; Cao, P. (PI) ; Cheriton, D. (PI) ; Cooper, S. (PI) ; Dally, B. (PI) ; De-Micheli, G. (PI) ; Dill, D. (PI) ; Dwork, C. (PI) ; Engler, D. (PI) ; Fedkiw, R. (PI) ; Feigenbaum, E. (PI) ; Fikes, R. (PI) ; Fisher, K. (PI) ; Fogg, B. (PI) ; Fox, A. (PI) ; Garcia-Molina, H. (PI) ; Genesereth, M. (PI) ; Gill, J. (PI) ; Girod, B. (PI) ; Goel, A. (PI) ; Golub, G. (PI) ; Guibas, L. (PI) ; Hanrahan, P. (PI) ; Heer, J. (PI) ; Hennessy, J. (PI) ; Horowitz, M. (PI) ; Johari, R. (PI) ; Johnson, M. (PI) ; Jurafsky, D. (PI) ; Katti, S. (PI) ; Kay, M. (PI) ; Khatib, O. (PI) ; Klemmer, S. (PI) ; Koller, D. (PI) ; Koltun, V. (PI) ; Konolige, K. (PI) ; Kozyrakis, C. (PI) ; Lam, M. (PI) ; Latombe, J. (PI) ; Leskovec, J. (PI) ; Levis, P. (PI) ; Levitt, M. (PI) ; Levoy, M. (PI) ; Li, F. (PI) ; Manna, Z. (PI) ; Manning, C. (PI) ; Mazieres, D. (PI) ; McCarthy, J. (PI) ; McCluskey, E. (PI) ; McKeown, N. (PI) ; Meng, T. (PI) ; Mitchell, J. (PI) ; Motwani, R. (PI) ; Musen, M. (PI) ; Nass, C. (PI) ; Nayak, P. (PI) ; Ng, A. (PI) ; Nilsson, N. (PI) ; Olukotun, O. (PI) ; Ousterhout, J. (PI) ; Parlante, N. (PI) ; Plotkin, S. (PI) ; Plummer, R. (PI) ; Prabhakar, B. (PI) ; Pratt, V. (PI) ; Raghavan, P. (PI) ; Rajaraman, A. (PI) ; Roberts, E. (PI) ; Rosenblum, M. (PI) ; Roughgarden, T. (PI) ; Sahami, M. (PI) ; Salisbury, J. (PI) ; Schwarz, K. (PI) ; Shoham, Y. (PI) ; Thrun, S. (PI) ; Tobagi, F. (PI) ; Trevisan, L. (PI) ; Ullman, J. (PI) ; Van Roy, B. (PI) ; Widom, J. (PI) ; Wiederhold, G. (PI) ; Williams, R. (PI) ; Winograd, T. (PI) ; Young, P. (PI) ; Zelenski, J. (PI)

CS 193C: Client-Side Internet Technologies

Client-side technologies used to create web sites such as sophisticated Web 2.0 interfaces similar to Google maps. XHTML, CSS, JavaScript, document object model (DOM), AJAX, and Flash. Prerequisite: programming experience at the level of 106A.
Terms: Sum | Units: 3
Instructors: Young, P. (PI)
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