These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Depending on the X department, enrollment in an additional Humanities capstone course may also be required. Course will focus on actual industry-based financial information from technology companies and realistic financial issues. All courses taken for the major must be taken for a letter grade if that option is offered by the instructor. Complete an honors thesis deemed acceptable by the thesis adviser and at least one additional faculty member. Recommended: CS 142 and/or CS 221. The PhD program in computer-based music theory and acoustics is offered by the Department of Music through the Center for Computer Research in Music and Acoustics (CCRMA, pronounced "karma"). Prerequisite: consent of instructor. After passing the required qualifying examination, each student must secure the agreement of a member of the department faculty to act as the dissertation adviser. AP Calculus Credit must be approved by the School of Engineering. J.D./M.S. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. In light of the current situation with the COVID-19 pandemic, Stanford reaffirms its commitment to perform individualized, holistic review of each applicant to its graduate and professional programs. Lectures, reading, and project. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. The Mathematics, Science, and Engineering Fundamentals requirements are non-standard for this track. CS 448V. Proficiency in some programming language, preferably Python, required. A key objective is for students to develop a basic set of skills to master day-to-day personal interactions, and to understand the dynamics of work environments. Interested students must also register for either EDUC 236 or CS 402, complete the application at bit.ly/BBA-Winter2020 by January 4 at 5 p.m., and come to the first class at 8:30 a.m. in CERAS 108. Science project presentation and computer science phd thesis structure. Prerequisites: CS106B/X and CS161, or permission from the instructor. Project-centric building hardware and software for embedded computing systems. Topics include knowledge tracing, generative grading, teachable agents, and challenges and opportunities implementing computational education in diverse contexts around the world. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. For CS graduate students. CS 191W. CS + Social Good Studio: Implementing Social Good Projects. Scientists in our Earth System Science department offer a strong graduate research program across a broad range of environmental and Earth science disciplines for students working toward a doctoral degree. Designing for accessibility is a valuable and important skill in the UX community. A follow up class to CS106A for non-majors which will both provide practical web programming skills and cover essential computing topics including computer security and privacy. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). Offices are located in Suites 127 (1st Floor) and 040 (Basement) Phone: 650-723-4284 This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. A project can be either a significant software application or publishable research. Admissions: admissions@cs.stanford.edu. Prerequisite: basic probability theory. Students can replace one of these electives with a course found at. Phone: 1 (650) 725-3140. Understand professional and ethical responsibility. Big Data is radically transforming healthcare. Because the team projects start in the first week of class, attendance that week is strongly recommended. For advanced undergraduates and for graduate students. Engineering Design Optimization. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. Introduction to Computing at Stanford. Recommended: CS 106B, CS 42 or 142. CS 234. 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. 3-4 Units. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Limited enrollment, permission of instructor required. These are big technical challenges. Register using the section number associated with the instructor. Both the adviser and the advisee are expected to maintain professionalism and integrity. Applicants to graduate studies in Computer Science must apply either to the MS program or to the Ph.D. program, and not to both. Also ideal for anyone with experience in front or back-end web development or human-computer interaction that would want to sharpen their visual design and analysis skills for UI/UX. GRE general test scores optional. Special Instructions: This class is limited to 65 students, with an effort made to have students from Stanford Law School (30 students will be selected by lottery) and students from Computer Science (30 students) and International Policy Studies (5 students). Writing-intensive version of CS182. Prerequisites: CS 107 or equivalent. Deep Learning is one of the most highly sought after skills in AI. Attend a weekly honors seminar Winter Quarter. Prerequisites: CS110 or EE102A. An introduction to computational complexity theory. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena. Topics include: behavioral finance, budgeting, debt, compensation, stock options, investing and real estate. Some students, however, may wish to complete the master’s program before deciding whether to pursue the Ph.D. To give such students a greater opportunity to become familiar with research, the department has a program leading to a master’s degree with distinction in research. Application required; please see cs51.stanford.edu for more information. This course will prepare students to interview for software engineering and related internships and full-time positions in industry. Proposals should include a minimum of 25 units and seven courses, at least four of which must be CS courses numbered 100 or above. 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). Today¿s successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. 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. Prerequisite: CS110. Biomedical Sciences Stanford MSTP students who are interested in PhD programs within the biomedical sciences, including those within the School of Medicine, the School of Humanities and Sciences (Departments of Biology, Chemistry, and Physics), and the School of Engineering (Bioengineering, Computer Science, Electrical Engineering, and more). For the first time, a joint Stanford Computer Science MS/MBA degree program will be available to graduate students in the 2014-2015 academic year. Additional problem solving practice for the introductory CS course CS 106A. The science elective may be any course of 3 or more units from the School of Engineering Science list (Fig. GRE general test scores required. We cover approaches towards motion planning and control using visual and tactile perception as well as machine learning. CS 348C. Concurrent enrollment in CS 161 required. 1 Unit. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. Capstone Biomedical Informatics (BMI) experience. Prerequisite: CS 107. We will also study applications of each algorithm on interesting, real-world settings. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. The department acknowledges that the student is a bona fide candidate for the Ph.D. and agrees that the program submitted by the student is sufficient to warrant granting the Ph.D. upon completion. Artificial Intelligence (AI) has the potential to drive us towards a better future for all of humanity, but it also comes with significant risks and challenges. Limited enrollment for this course. Students will learn techniques for rapid prototyping of smart devices, best practices for physical interaction design, fundamentals of affordances and signifiers, and interaction across networked devices. Computers, Ethics, and Public Policy. 3-4 Units. CS 236. Advanced Multi-Core Systems. Seminar goal is to expose students from engineering, medicine, and business to guest lecturers from academia and industry. This course provides a comprehensive introduction to interactive computer graphics, focusing on fundamental concepts and techniques, as well as their cross-cutting relationship to multiple problem domains in interactive graphics (such as rendering, animation, geometry, image processing). Linear and non-linear dimensionality reduction techniques. CS 231C. 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. CS 58N. CS 254 recommended but not required. Algorithmic Techniques for Big Data. One of the following: EE 101A, 101B, 102A, 102B, Digital Systems Concentration: CS 140 or 143; EE 109, 271; plus one of CS 140 or 143 (if not counted above), 144, 149, 240E, 244: EE 273, 282, Robotics and Mechatronics Concentration: CS 205A, 223A; ME 210; ENGR 105, Networking Concentration: CS 140, 144; plus two of the following, CS 240, 240E, 244, 244B, 244E, 249A, 249B, EE 179, EE 276, A program of 21 units must be completed. 3-5 Units. By precisely asking, and answering such questions of counterfactual inference, we have the opportunity to both understand the impact of past decisions (has climate change worsened economic inequality?) Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Prerequisite: 110. No biological background assumed. 3-5 Units. If students have fulfilled the six breadth-area requirements, and taken courses from at least four different faculty who are members of the Academic Council, they are eligible to apply for candidacy prior to the second year in the program. The course culminates with students forming project teams to create a final video game. About. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. School of Engineering . Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. prior experience training a deep learning model). Students will read and discuss published research papers and complete an original research project. in Computer Science is the greater of: (i) 12 units; or (ii) the maximum number of units from courses outside of the department that M.S. 4 Units. 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. CS 147. Through course work and guided research, the program prepares students to make original contributions in Computer Science and related fields. Emphasis is on good programming style and the built-in facilities of respective languages. Complex problems require sophisticated approaches. Students should consult their department or program's student services office for applicability of Overseas Studies courses to a major or minor program. This course focuses on the foundational concepts that drive these applications. CS 273B. 3-4 Units. Several popular video manipulation algorithms will be presented, with an emphasis on using these techniques to build practical systems. 3 Units. Students taking CME 104 Linear Algebra and Partial Differential Equations for Engineers are also required to take its prerequisite, CME 102 Ordinary Differential Equations for Engineers. This is an implementation-heavy, lab-based class that continues the topics from CS240LX. CS 101. (Solving an open problem is not required!) Besides those solvable in principle, what problems can we hope to efficiently solve? May be repeated for credit. Prerequisite: consent of instructor. Recommended: EE261, EE278. In this course, we will see several classical and recent results on metric embeddings with a focus on algorithmic applications. Advanced Topics in Formal Methods. Deep Generative Models. Client-side topics include layout and rendering through HTML and CSS, event-driven programming through JavaScript, and single-threaded asynchronous programming techniques including Promises. Introduces the essential ideas of computing: data representation, algorithms, programming "code", computer hardware, networking, security, and social issues. Note that if CS145 was waived in area (A), students should take an additional course from either area (B) or (C) in its place. Our code will run "bare-metal" (without an operating system) on the widely-used ARM-based raspberry pi. CS 251. See Handbook for Undergraduate Engineering Programs for further information. The course covers classical computer-aided design, geometry processing, and data-driven approaches for shape generation. Judges sentence defendants based on statistical risk scores; regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models.

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