This course will study statistical machine learning methods for analysing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. Phone: (650) 723-2300 Admissions: email@example.com. Specific topics include: incentives, ethics, crypto-commons, values, FOMO 3D, risks, implications and social good. This course is designed as a deep dive into the design, analysis, implementation, and theory of data structures. Intersection and visibility problems. CS 111 and CS 140 cannot both be counted towards the BS requirements. Hacking the Pandemic. Animating natural phenomena. The Departments of Computer Science and Philosophy offer a joint major program (JMP) for undergraduates who wish to gain mastery and develop skills in these two disciplines. Limited class size. 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. The first digit of a CS course number indicates its general level of sophistication: The tens digit indicates the area of Computer Science it addresses: The department offers both a major in Computer Science and a minor in Computer Science. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. CS 224U. Novel interface technology, advanced interface design methods, and prototyping tools. CS 371. Mathematical Problem-solving Strategies. Recommended that CS Majors have also taken one of 142, 193P, or 193A. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. CS 110L. Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. 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. Modeling Biomedical Systems. 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. CS 234. CS 43. 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. 3-4 Units. Not regular? Prerequisites: proficiency in python, CS 229 or equivalents or permission of the instructor; linear algebra, basic probability. Students who don't need the full year to find a professor to align with will have the option of aligning within the first or second quarter. Many fundamental principles, key technologies and important applications lie at the intersection between the two disciplines. For Ph.D. requirements, courses in which students receive a grade of ‘S’ or ‘CR’ can be counted toward program requirements with the exception of the Breadth requirement. This lab course is a hands-on introduction to the prototyping and fabrication of tangible, interactive technologies, with a special focus on learning and education. Most students planning to obtain the Ph.D. degree should apply directly for admission to the Ph.D. program. Introduction to linear programming. 1 Unit. The intent is to allow the first-year Ph.D. student to work with a variety of professors before aligning with a permanent program adviser. in the case of a particular student's individual program. You'll learn how to use the Unity game engine, the most popular platform for creating immersive applications. Letter grade only. Cloud Computing Seminar. We'll start with the basic questions like who decides who gets to see themselves as "a computer person," and how do early childhood and educational experiences shape our perceptions of our relationship to technology? Teaching Computer Science. 3-4 Units. Prerequisites: CS110 for undergraduates, EE282 for graduate students. This project-based course provides a survey on designing and engineering video games. The course will introduce ideas from computational genomics, machine learning and natural language processing. When most effective, this advising relationship entails collaborative and sustained engagement by both the adviser and the advisee. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin. 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. The course will be self-contained, and no prior experience with randomness/probability is necessary. Computation and Cognition: The Probabilistic Approach. Course includes primers on molecular biology, and text processing languages. Educational opportunities in high technology research and development labs in the computing industry. CS 261. Join us as we go behind the scenes of some of the big headlines about trouble in Silicon Valley. CS 369L. Topic changes each quarter. Working on a real-world challenge related to social health and civic fabric (e.g. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. Students in CS 106A need not have prior programming experience. We will develop the theory behind these models and algorithms and then apply them to real datasets in the homeworks and final project.This course is similar to STATS215: Statistical Models in Biology and STATS366: Modern Statistics for Modern Biology, but it is specifically focused on statistical machine learning methods for neuroscience data. CS 298. Technical developments in artificial intelligence (AI) have opened up new opportunities for entrepreneurship, as well as raised profound longer term questions about how human societal and economic systems may be reorganized to accommodate the rise of intelligent machines. 3 Units. Introduction to Computer Graphics and Imaging. We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review. Hierarchies of Integer Programming Relaxations. Prerequisites: CS161 or equivalent, STAT116 or equivalent. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Our exploration will look both at the principles behind the discoveries and inventions, as well as the history and the people involved in those events. The fundamentals of cross-platform mobile application development using the React Native framework (RN). Letter grade; if not appropriate, enroll in CS199P. Hands-on laboratory course experience in robotic manipulation. The student’s adviser has primary responsibility for the adequacy of the program, which is subject to review by the Student Services Office. Embedded Systems Workshop. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. Intended for students who are pursuing a focus on HCI, this course focuses on showing students how HCI gets applied in industry across different types of companies. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization. Each of the three Breadth courses must be from different Areas: CS 181, CS 182, CS 384; AMSTUD 133, AMSTUD 145; ANTHRO 132D; COMM 120W, COMM 124, COMM 145, COMM 154, COMM 166, COMM 186W, COMM 230A, COMM 230B, COMM 230C; DESINST 215, DESINST 240; ENGLISH 184D; ENGR 248; HISTORY 244F; LINGUIST 230A; ME 177; MS&E 193, MS&E 231, MS&E 234, MS&E 254; POLISCI 150A; PSYCH 215; PUBLPOL 103F. Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. We will also cover effective coaching, design of projects and exercises, and curating material in order to maximize the effectiveness of a flipped classroom. CS 390C. CS 325B. Same as: AA 222. Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. One concern with the rise of such algorithmic decision making is that it may replicate or exacerbate human bias. CS 348B. CS 273A. CS 294S. A representation performs the task of converting an observation in the real world (e.g. Computer Vision: From 3D Reconstruction to Recognition. CS 254B. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. A laptop computer is recommended for the in-class exercises. Departmental Lecture Series. Topics: data mining, machine learning, data ingest, and data transformations using Hadoop, Spark, Apache Impala, Apache Hive, Apache Kafka, Apache Sqoop, Apache Flume, Apache Avro, and Apache Parquet. CS 349F. Limited enrollment, permission of instructor, and application required. No prior finance or economics experience required. Prerequisites: basic probability theory and algorithm design and analysis. CS 197. The course consists of on-site visits to large companies (for example Google, Yahoo, Square, Tesla) and to startups to talk to the HCI practitioners at these companies and learn first hand how HCI and design fits in at different companies. 1 Unit. Advanced User Interface Design Patterns. CS 294W. For example, would you ever let an important choice depend on the flip of a coin? All PhD students in good standing receive financial support, including a living stipend/salary, full tuition, and a health insurance subsidy. Use of LP duality for design and analysis of algorithms. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. May be repeated for credit. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise. 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. Recent advances in computing may place us at the threshold of a unique turning point in human history. Prerequisites: AA 228/CS 238 or CS 221. 2 Units. The Applied Crypto Group is a part of the Security Lab in the Computer Science Department at Stanford University.Research projects in the group focus on various aspects of network and computer security. Financial systems have spurred technological innovation and, in turn, are driven byncutting-edge technological developments. Select at least four of the following: B. Natural Language Understanding. Permission number required for enrollment; see the CS PhD program administrator in Gates room 195. 3-4 Units. No coding involved; however we will be deeply exploring the human operating system. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. In addition to Computer Science itself, Stanford offers several interdisciplinary degrees with a substantial computer science component. Prerequisite: CS 106B; recommended: CS161; consent of instructor for 3 units. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Students will work in groups on a final class project using real world datasets. CS 236G. This lab is a group of professors in the Computer Science department with a common interest in advancing education using computer science techniques. Includes a project. Prerequisites: CS107 and CS161. For seniors and first-year graduate students. Students will be exposed to integrated knowledge and techniques across computer graphics, robotics, machine learning and biomechanics. This program is described in more detail below. The PhD program in epidemiology and clinical research will provide methodologic and interdisciplinary training that will equip students to carry out cutting-edge epidemiologic research. 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