University policy requires that all doctoral students declare candidacy by the end of the sixth quarter in residence, excluding summers. Research assistants (RAs) help faculty and senior staff members with research in computer science. 353 Jane Stanford Way Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Same as: BIODS 472, BIOMEDIN 472. Computer Science Research. In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. CS 390A, CS390B, and CS390C may each be taken once. Curricular Practical Training. CS 202. In this course, we will discuss several success stories at the intersection of algorithm design and machine learning, focusing on devising appropriate models and mathematical tools to facilitate rigorous analysis. Automated Reasoning: Theory and Applications. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Proceedings of the 2019 International Conference on Foundations of Computer Science (FCS'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada. Uses the programming language C++ covering its basic facilities. Same as: MED 253. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Topics in Computer Graphics: Computational Video Manipulation. Computational Methods for Biomedical Image Analysis and Interpretation. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can benleveraged to learn more efficiently or effectively. Mining Massive Data Sets Hadoop Lab. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. Course surveys the legal and ethical principles for assessing the equity of algorithms, describes statistical techniques for designing fairer systems, and considers how anti-discrimination law and the design of algorithms may need to evolve to account for machine bias. Enrollment in WIM version of the course is limited to 120 students. To apply for the honors program, students must be majoring in Computer Science, have a grade point average (GPA) of at least 3.6 in courses that count toward the major, and achieve senior standing (135 or more units) by the end of the academic year in which they apply. Seminar in Artificial Intelligence in Healthcare. 2 Units. Then we will critically examine current models that are used to predict infection rates in the population as well as models used to support various public health interventions (e.g. Intensive version of 106B for students with a strong programming background interested in a rigorous treatment of the topics at an accelerated pace. However, it is acceptable to count both CS 111 and CS 140E towards the BS requirements. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. For University-wide policy changes related to the pandemic, see the "COVID-19 and Academic Continuity" section of this bulletin. Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Features weekly lectures and a series of small programming projects. 3-4 Units. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material. Cryptocurrencies and blockchain technologies. No prior finance or economics experience required. The PDF will include all information unique to this page. Online Bachelor's in Computer Science University of Maryland Global Campus. The program prepares students for careers in government, law, the corporate sector, and for graduate study. Topics include operating systems, networking, security, troubleshooting methodology with emphasis on Stanford's computing environment. Emphasis will be placed on building novel machines and related software for use by "makers" and interactive machines. Designing Solutions to Global Grand Challenges. 3-5 Units. Prerequisites: CS 110 and CS 161. 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 325B. 3-4 Units. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. There are no course prerequisites, and no prior legal or technical training will be assumed. Admission to the joint MSCS/MBA program requires that students apply and be accepted independently to both the Computer Science Department in the School of Engineering and the Graduate School of Business. Limited class size. Students will also gain experience with key technologies for the creation of autonomous robots, including perception, action, human-robot interaction, and learning. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. It includes classical concepts that are still widely used and recent approaches that have changed the way we look autonomous manipulation. Introduction to linear programming. CS 106AX. CS 421. A playback show brings about a powerful listening and sharing experience. CS 294W. Continuation of CS51 (CS + Social Good Studio). We will study the basic tools pseudorandomness, such as limited independence, randomness extractors, expander graphs, and pseudorandom generators. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Students will learn how to implement data mining algorithms using Hadoop and Apache Spark, how to implement and debug complex data mining and data transformations, and how to use two of the most popular big data SQL tools. This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and computational photography. Students will read and discuss papers, and do programming projects. The student is expected to demonstrate the ability to present scholarly material orally in the dissertation defense. This course focuses on giving quantum software engineering industry experience with open-source projects proposed by frontier quantum computing and quantum device corporate partners.Quantum computing and quantum information industry sponsors submit open-source projects for students or teams of students to build and create solutions throughout the quarter with mentorship from the company. Wellness in Tech: Designing an Intentional Lifestyle in a Tech-Driven World. Additional problem solving practice for the introductory CS course CS109. With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. In the second part, we explore models and algorithms for simulation and robot learning in multi-agent domains and human-robot interaction, studying the principles of learning for interactive tasks in which each agent collaborates to accomplish tasks. Please check them out at https://ai.stanford.edu/stanford-ai-courses Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Additional problem solving practice for the introductory CS course CS106B. Animating natural phenomena. Same as: AA 222. Students may also consult the Student Services Center with questions concerning dropping the joint major. Proposals should include a minimum of 25 units and seven courses, at least four of which must be CS courses numbered 100 or above. The topics include domains of social navigation, human-robot collaborative manipulation and multi-agent settings.nnThis a project-based seminar class. Computational Complexity II. We will look at what makes a good or bad user interface, effective design techniques, and how to employ these techniques using Sketch and Marvel to make realistic prototypes. The Ph.D. is conferred upon candidates who have demonstrated substantial scholarship and the ability to conduct independent research. CS 109A. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. The primary focus is on developing best practices in writing Python and exploring the extensible and unique parts of the Python language. But it also has the potential to exacerbate human biases, destroy trust in information flow, displace entire industries, and amplify inequality throughout the world. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. As part of the training for the Ph.D., the student is also required to complete at least four units (a unit is ten hours per week for one quarter) as a course assistant or instructor for courses in Computer Science numbered 100 or above. Same as: CME 309. ), check out Nick's experimental javabat.com server, where you can type in little code puzzles and get immediate feedback.. Pointers and Memory-- videos and materials on basic pointers The Senate decided that all undergraduate and graduate courses offered for a letter grade must also offer students the option of taking the course for a “credit” or “no credit” grade and recommended that deans, departments, and programs consider adopting local policies to count courses taken for a “credit” or “satisfactory” grade toward the fulfillment of degree-program requirements and/or alter program requirements as appropriate. Candidacy expires five years from the date of submission of the candidacy form, rounded to the end of the quarter. Elective courses that also satisfy a Breadth requirement must be taken for a letter grade. CS 224S. Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. The teaching team and teaching assistants will work closely with students on research projects in this area. Prerequisite: CS 51, or consent of instructor. Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Students who have taken both MATH 51 and MATH 52 may not count CME 100 as an elective. A growing field in deep learning research focuses on improving the Fairness, Accountability, and Transparency (FAccT) of a model in addition to its performance. Students must apply for the class by filling out the form at https://goo.gl/forms/9LSZF7lPkHadix5D3. This is a crash course in how to use a stripped-down computer system about the size of a credit card (the rasberry pi computer) to control as many different sensors as we can implement in ten weeks, including LEDs, motion sensors, light controllers, and accelerometers. 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). Proposals should include a minimum of 25 units and seven courses, at least four of which must be CS courses numbered 100 or above. Prerequisite: CS 107, MATH 51. Courses offered by the Department of Computer Science are listed under the subject code CS on the Stanford Bulletin's ExploreCourses web site. The book was developed from an industrial training course for engineers in industries that manufacture biological products and is presented in a manner that requires only a minimum background in the biological sciences. The course will introduce ideas from computational genomics, machine learning and natural language processing. MATH 19, MATH 20, and MATH 21, or AP Calculus Credit may be used as long as at least 26 MATH units are taken. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. Worst and average case analysis. 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. Cloud Computing Seminar. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses. Other Classes at Stanford University In this seminar, we deeply examine these themes in medical CV research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and healthcare. Students can replace one of these electives with a course found at: One additional course from the list above or the following: At least two courses from the general CS electives list. 3 Units. Use of database … Plus three of the following (9-11 units): Track Electives: at least two additional courses from the lists above, the general CS electives list, or the courses listed below. However, after aligning with a permanent adviser, passing six breadth requirements, and taking classes with four different faculty, a student is eligible to file for candidacy prior to the sixth quarter. 3 Units. CS 182. Much progress in this area in recent years has been motivated by algorithmic applications. Computer Vision: Foundations and Applications. 2 Units. 3 Units. Each student will help lead a section; the class collectively will produce a final book/movie/blog, in a medium selected by the class. Same as: COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182. Stanford University. 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?. The course will accompany the projects with basic insights on the main ingredients of research. May be repeated for credit. Students will also conduct a group research projects in this field.nnPrerequisites: Sufficient mathematical maturity to follow the technical content; some familiarity with data mining and machine learning and at least an undergraduate course in statistics are recommended. CS 244. 3 Units. CS 229 may be taken concurrently. 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. Minimum Combined GPA for all courses in Engineering Fundamentals and Depth is 2.0. CS 145. Prerequisites: MATH 51; Math104 or MATH113 or equivalent or comfort with the associated material. Specific topics include: incentives, ethics, crypto-commons, values, FOMO 3D, risks, implications and social good. Pre/Corequisite: CS106B or CS106X. CS108) and/or functional programming languages (e.g. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. Supplemental lab to CS 110. Present the thesis at a public colloquium sponsored by the department. Advanced Multi-Core Systems. Prerequisites: 106B or X, or consent of instructor. From the Registrar's Office: Students . 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. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. In the final 3-4 weeks of the class, teams will participate in an open-ended design challenge. CS 91SI. CS 251. Supplemental lab to 106B and 106X. This class is cross-listed in the University and undergraduates and graduates are eligible to take it. degree in Computer Science. CS 194H. Prerequisite: CS147 or equivalent. Design and Analysis of Algorithms. Students must be co-enrolled in CS106B. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. CS229) and basic neural network training tools (eg. Thesis advisers must be members of Stanford’s Academic Council. CS 273A. Prerequisite: consent of instructor. Become familiar with prototype-design tools like Sketch and Marvel while also learning important design concepts in a low-stress environment. 3-4 Units. Students should have a desire to make things. The focus of CS247A is design for human-centered artificial intelligence experiences. Prerequisites: 103, 110. Writing-intensive version of CS191. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Basic knowledge of probability, linear algebra, and calculus. Designing AI to Cultivate Human Well-Being. Focus is on techniques used to teach topics covered in CS106B. This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. Possible additional topics: network flow, string searching. Simplicity and Complexity in Economic Theory. Abstraction and its relation to programming. 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. Prerequisite: CS 107. Recommended that CS Majors have also taken one of 142, 193P, or 193A. Same as: STATS 214. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Additional problem solving practice for the introductory CS course CS107. 1 Unit. A project can be either a significant software application or publishable research. Same as: VPTL 196. Continuous Mathematical Methods with an Emphasis on Machine Learning. Handling genomic data is deceptively easy. Prerequisite: 106A or equivalent. Prerequisite: CS106A. Undergraduate students should enroll in CS199; PhD students should enroll in CS499. Use of database management or file systems for a substantial application or implementation of components of database management system. I don't have the official … Contents change each quarter. The following core courses fulfill the minor requirements. J.D./M.S. Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. A total of at least 21 units from category (A) and at least 3 of the following: C. A total of at least 21 units from categories (A), (B) and the following: A. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques. Same as: EDUC 234A. Learn from Stanford … Select two courses, each from a different area: Select one additional course from the Areas above or from the following: Track Electives: at least three additional courses selected from the Areas and lists above, general CS electives, or the courses listed below. Given class size limitations, an online survey will be used to achieve a diverse class composition. If the student fails the qualifying exam a second time, the Ph.D. program committee is convened to discuss the student's lack of reasonable academic progress. Select two courses, each from a different area: Select one additional course from the Areas above or from the following: Track Electives: at least three additional courses selected from the Areas and lists above, general CS electives, or the courses listed below. Over the last few years we have seen the rise of "serious games" to promote understanding of complex social and ecological challenges, and to create passion for solving them. Learn more about Computer Science in the Stanford Bulletin. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. Recent topics: computational photography, datanvisualization, character animation, virtual worlds, graphics architectures, advanced rendering. Restricted to Computer Science and Computer Systems Engineering undergraduates. Digital Technology and Law: Foundations. 2 Units. By Spring Quarter of the second year, a student should complete all six breadth area requirements, two breadth area requirements in each of three areas, and file for candidacy. Application required; please see cs51.stanford.edu for more information. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Classic and new papers. Bare-metal lets us do interesting tricks without constantly fighting a lumbering, general-purpose OS that cannot get out of its own way. Course offered occasionally. A student must fulfill two breadth-area requirements in each of three general areas by the end of the second year in the program. See Fig. Letter grade; if not appropriate, enroll in CS399P. Either of the PHYSICS sequences 61/63 or 21/23 may be substituted for 41/43 as long as at least 11 science units are taken. Students will be introduced to design guidelines for integrating electrical components such as PCBs into mechanical assemblies and consider the physical form of devices, not just as enclosures but also as a central component of the smart product. This is an experimental hands-on laboratory class, and our direction may shift as the creative needs of the theatrical production evolve. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. Copyright Complaints Otherwise, elective courses may be taken on a satisfactory/no credit basis provided that a minimum of 36 graded units is presented within the 45-unit program. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Topics in Artificial Intelligence. The Engineering Physics program is designed for students who have an interest in and an aptitude for both engineering and physics. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. CS 351. degree as specified in this Bulletin. Found insideReveals how established attitudes affect all aspects of one's life, explains the differences between fixed and growth mindsets, and stresses the need to be open to change in order to achieve fulfillment and success. Drawing on multiple sources of actual interview questions, students will learn key problem-solving strategies specific to the technical/coding interview. Example applications to robotic motion planning, visibility preprocessing and rendering in graphics, and model-based recognition in computer vision. The fundamentals and state-of-the-art in web security. Software -- Programming Languages. The class will also include a guest speaker who will give teaching advice and talk about AI. CS193p - Developing Apps for iOS. This course will require you to learn a new programming language (Swift) as well as a new-to-iOS development environment, SwiftUI. 1-4 Unit. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. In doing so, we'll see a number of classic data structures like Fibonacci heaps and suffix trees as well as more modern data structures like count-min sketches and range minimum queries. CS 571. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons.
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