Introduction to Probability for Computer Scientists. Additional problem solving practice for CS161. How can we ensure that AI technology will help reduce bias in human decision-making in areas from marketing to criminal justice, rather than amplify it?. herd immunity and social distancing). Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Visit the Graduate Student Gateway for information about enrollment, financial aid, housing and more. Novel interface technology, advanced interface design methods, and prototyping tools. CS 240. CS 228. While understanding users is one part of the equation, the other part is a strong understanding of user interface design rules and patterns that you can apply to solve their needs. It is the university's home for fundamental research, where free, open, and critical inquiry is pursued across disciplines. Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. At least three of the courses must be master’s core courses to provide breadth and one course numbered 300 or above to provide depth. CS 257. Concepts will be developed in part through guided in-class coding exercises. 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). 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. Given class size limitations, an online survey will be used to achieve a diverse class composition. Many fundamental principles, key technologies and important applications lie at the intersection between the two disciplines.The intersection of EE and CS is addressed by many courses, including those listed below. 3-4 Units. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. CS 544. CS 344. Digital Image Processing. We will explore some of the major problems in this area from the viewpoint of industry and academia. What makes computers fast? Deep learning on irregular geometric data. In addition to their dominant roles in entertainment, video games play growing roles in education, arts, and science. The University’s basic requirements for the Ph.D. degree are outlined in the “Graduate Degrees” section of this bulletin. 3-4 Units. For CS graduate students. Students may apply for admission starting this fall. Through creating their own games each week, students explore topics including 2D/3D Art, Audio, User Interface design, Production, Narrative Design, Marketing, and Publishing. Prerequisites: Appropriate preparation depends on the nature of the project proposed, and will be verified by the teaching staff based on your application. Topics include: 2D and 3D drawing, sampling theory, interpolation, rasterization, image compositing, the real-time GPU graphics pipeline (and parallel rendering), VR rendering, geometric transformations, curves and surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, image processing, time integration, physically-based animation, and inverse kinematics. CS + Social Good Studio: Designing Social Impact Projects. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Servers and workstations running Linux , MacOS, or various versions of Windows are commonplace. Arrangements of curves and surfaces. Failing the exam a second time is cause for dismissal from the Computer Science Ph.D. program and the committee meets to discuss the final outcome for the student. We will also study applications of each algorithm on interesting, real-world settings. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. CS 224W. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. All courses taken for the major must be taken for a letter grade if that option is offered by the instructor. Students may apply for admission starting this fall. statistics, CS221, CS230, CS229). CS 241. CS193Q teaches basic Python programming with a similar end-condition to CS106AP: strings, lists, numbers, dicts, loops, logic, functions, testings, decomposition and style, and modules. David Packard Building 350 Jane Stanford Way Stanford, CA 94305. Kirjallisuuden opetuksesta petroskoin valtionyliopiston it merensuomalaisten kielten ja kulttuurin tiedekunnassa. Methods for processing human language information and the underlying computational properties of natural languages. A hands-on interactive and fun exploration of great ideas from computer graphics. 3 Units. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. There will be an information session (announced in CS246) explaining the datasets available in early March and this information will also be on the CS341 course website in late February. Limited enrollment, permission of instructor required. The Practice of Theory Research. Computational Models of the Neocortex. Linear and non-linear dimensionality reduction techniques. Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. The student must pass a University oral examination in the form of a defense of the dissertation. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, and storytelling through virtual reality. Prerequisites: linear algebra, statistics, CS106B, plus a graduate-level AI course such as: CS230, CS229 (or CS129), or CS221. Advanced Topics in Operating Systems. This seminar series brings together a diverse set of experts to provide interdisciplinary perspectives on these media regarding their history, technologies, scholarly research, industry, artistic value, and potential future. You will master not only the theory, but also see how it is applied in industry. requirements, courses in which students receive a grade of ‘S’ or ‘CR’ can be counted toward program requirements as if taken for a letter grade. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry. Register using instructor's section number. Understand professional and ethical responsibility. 1 Unit. Each accepted team will be assigned a mentor who will work with them regularly throughout the quarter. Educational opportunities in high technology research and development labs in the computing industry. The following are general department requirements. Same as: MUSIC 254. Prerequisites: Probability (CS 109), linear algebra (MATH 113), machine learning (CS 229), and some coding experience. Unlike the double major or dual major, the Joint Major emphasizes integration of the two fields through a cohesive, transdisciplinary course of study and integrated capstone experience. Prerequisite: 248 or equivalent. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. Generative Adversarial Networks (GANs) have rapidly emerged as the state-of-the-art technique in realistic image generation. CS 472. Hands-on laboratory course experience in robotic manipulation. CS 325B. Same as: BIODS 472, BIOMEDIN 472. Standard C++ Programming Laboratory. The Senior Project is fulfilled with a joint capstone project. This seminar class introduces students to major problems in AI explainability and fairness, and explores key state-of-theart methods. Advanced Topics in Formal Methods. Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications. What computers are and how they work. Directed research under faculty supervision. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Graded satisfactory/no credit. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Same as: LINGUIST 285. Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. No prior programming experience required. Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Two of the depth electives are waived. As many as 45 quarter units of approved courses may be … Software engineering basics. The computer science department continues to lead the world in computer science research and education. degree in Computer Science is intended as a terminal professional degree and does not lead to the Ph.D. degree. CS 547. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. 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. Same as: COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182. Concurrent enrollment in CS 107 required. Apply the knowledge of mathematics, science, and engineering. 2 Units. degree, and the Computer Science Department approves courses from the Law School that may count toward the M.S. This course provides an overview of the many and varied IP issues that students will confront during their careers. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Prerequisites: CS161 and CS154. The goal of this graduate (advanced undergraduate also welcome) course is to survey recent work on computational video analysis and manipulation techniques. This course will focus on the algorithmic and statistical methods needed to approach FAccT AI from a deep learning perspective. This project-based course will explore the field of computational journalism, including the use of Data Science, Info Visualization, AI, and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech, and build trust. Function spaces and functional maps. The course culminates with students forming project teams to create a final video game. Topics in Computer Networks. Recommended as a preparation for dissertation research. The application must include a letter describing the research project, a letter of endorsement from the faculty sponsor, and a transcript of courses taken at Stanford. The Department of Computer Science (CS) offers an honors program for undergraduates whose academic records and personal initiative indicate that they have the necessary skills to undertake high-quality research in computer science. High-level Vision: From Neurons to Deep Neural Networks. 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. CS 197. Limited enrollment for this course. This course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fair systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Recommended Prerequisites: CS248, CS231N, CS229, CS205A. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. Register using the section number associated with the instructor. Same as: LINGUIST 180, LINGUIST 280. Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. Consult the department web site for details. Same as: STATS 214. CS 273A. What are the principles of human flourishing, and what is technology's role in promoting them? Today¿s successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. 1 Unit. 3 Units. 1 Unit. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, MapReduce, Apache Spark, SPMD, message passing, SIMT, transactions, and nested parallelism). But non-technical skills are just as critical to making a difference. In general, the M.S. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. How do we design these social computing systems to be effective and responsible? CS 248. These tracks also reflect the broad diversity of areas in computing disciplines. The dissertation must be accepted by a reading committee composed of the principal dissertation adviser, a second member from within the department, and a third member chosen from within or outside of the University. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. In this hands-on project-based course, students will learn about future opportunities and present realities for autonomous robots that provide physical assistance to humans. In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Credit for coursework done elsewhere (up to the maximum of 45 course units) may be applied to graduation requirements. The Department of Electrical Engineering and Computer Sciences (EECS) offers two graduate programs in Computer Science: the Master of Science (MS), and the Doctor of Philosophy (PhD). This course takes place entirely in studios; you must plan on attending every studio to take this class. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. CS 51. Those students who have waived out of CS 221 may take an additional course in either area (B) or (C). 3 Units. Same as: ME 320. Advanced Topics in Cryptography. This course targets an audience with prior programming experience, and that prior experience is leveraged so material can be covered in greater depth. For course descriptions and additional offerings, see the listings in the Stanford Bulletin's ExploreCourses web site (http://explorecourses.stanford.edu) or the Bing Overseas Studies web site (http://bosp.stanford.edu). The course introduces the basics of quantum algorithms, quantum computational complexity, quantum information theory, and quantum cryptography, including the models of quantum circuits and quantum Turing machines, Shor's factoring algorithms, Grover's search algorithm, the adiabatic algorithms, quantum error-correction, impossibility results for quantum algorithms, Bell's inequality, quantum information transmission, and quantum coin flipping. The Department of Computer Science (CS) operates and supports computing facilities for departmental education, research, and administration needs. This program is described in more detail below. CS 379C. CS 107A. This class will culminate in an open-ended final project, which the teaching team will help you on. CS 251. The student must present an oral thesis proposal and submit the form to their full Reading Committee by Spring Quarter of the fourth year. CS 101 and CS 105 meet the Ways of Thinking Ways of Doing breadth requirements in Formal Reasoning and include an introduction to programming and the use of modern Internet-based technologies. Requirements: Fluency in Unix shell and Python programming; familiarity with differential equations, linear algebra, and probability theory; priori experience with modern machine learning concepts (e.g. They’ll do the research and the writing… and prepare you to defend your dissertation! All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Prerequisites: CS 147, or permission of instructor. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students will read recent research papers and complete a design project. May be repeated for credit. Readings will include classic papers along with additional explanatory material. Information Retrieval and Web Search. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Designing for Accessibility. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. CS 205L. What problems can be solved with computers? Supplement to CS 246 providing additional material on the Apache Hadoop family of technologies. 3-4 Units. CS 110A. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. This course introduces basic logic programming theory, current technology, and examples of common applications, notably deductive databases, logical spreadsheets, enterprise management, computational law, and game playing. 3 Units. Final project. Prerequisite: CS106A. Geometry processing on meshes, including simplification and parametrization. Several such hierarchies are well-studied in the literature: Lovasz-Schrijver, Sherali-Adams and the Parrilo-Lasserre sum-of-squares (SoS) hierarchy. CS 270. Introduction to Computing Principles. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. Speakers will underscore the need to innovate to survive in the media and information industries. Computational Law is an innovative approach to legal informatics concerned with the representation of regulations in computable form. Character Animation: Modeling, Simulation, and Control of Human Motion. Prerequisite: CS110. CS 227B. CS 298. Over the years, many powerful algorithms have been built via tools such as linear programming relaxations, spectral properties of graphs, and others, that all bridge the discrete and continuous worlds. Law, Order, & Algorithms. Introduction to Biomedical Informatics Research Methodology. Students are expected to produce an original research paper on a relevant topic. 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