41 - 50 of 236 results for: MS

The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent.

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in EE290A; for 290C, EE PhD degree candidacy and an "S" grade in EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in EE 290C and instructor consent; for 290E, EE PhD degree candidacy, an "S" grade in EE 290D and instructor consent.

The Internet is at an inflection point. As AI and synthetic media explode, the world's digital knowledge faces unprecedented threats. At the same time, a new generation of web technologies known as "Web3" offer new opportunities to protect the security and integrity of data. Our class jumps into this high-stakes moment and equips students with a new framework to understand and deploy methods to restore trust in digital content whether it's news and information, legally admissible evidence, or tamper-proof archives. Open to students of all experience levels, this class will provide an introduction to how advances in cryptography and the decentralized web can allow users to establish the provenance and veracity of data as it moves online. Students will create end-to-end technical prototypes and emerge with a new understanding that authenticity isn't a guaranteed part of information systems. You have to design for authenticity. Open to PhD, MS, and advanced undergraduate students.

Development of modern technologies such as 5G wireless networks, terabit silicon photonic optical interconnects, and optical computing relies on a deep understanding of the underlying electromagnetic principles governing their operation. Engineers must rely on numerical simulations to predict and model their behaviors when designing these systems. This class will give a graduate-level introduction to computational methods for solving partial differential equations describing physical phenoma that commonly arise in the real world. Primarily finite difference methods, in both the time and frequency domains, will be covered, although integral equation-based approaches and finite element methods will be introduced well. The course will also introduce modern inverse design approaches for automating the design of new electromagnetic structures, including gradient-based methods and the adjoint method, as well as global search strategies. Numerous examples drawing from practical applications, primarily in electromagnetics, will be presented for solving relevant real-world problems, including radiating antennas for wireless communication, dielectric waveguides for nanophotonic integrated circuits, as well as electromagnetic scattering from arbitrary dielectric objects for applications in radar scattering and remote sensing. Open to PhD, MS, and advanced undergraduate students.

VISITING: Open to visitors. TYPE OF CLERKSHIP: Elective. DESCRIPTION: Point-of-care ultrasound (POCUS) has become increasingly utilized in multiple medical specialties, with emergency medicine at the forefront. This rotation is designed to introduce point-of-care ultrasound to the clinical medical student and provide more in-depth and hands-on familiarity with bedside US for a variety of modalities. These skills will better equip students to use these techniques at the bedside of any patient in the emergency room or on the floors with greater facility and confidence. It will enhance patient diagnosis and management, procedural guidance, and patient satisfaction. It may even save a life! Primary emphasis will be on developing competent technical skills to enhance image acquisition and interpretation. The applications as defined by the American College of Emergency Physicians will be the main focus. A goal for a minimum number of ultrasound scans will be 25 per application including, eFAST, thoracic, renal, RUQ, aorta, limited ECHO and IVC, first trimester pelvic, DVT, orbital, MSK. Other advanced ultrasound applications will potentially be introduced (airway, bladder, nerve blocks). Students will obtain US images in the Stanford Emergency Department and will have imaging formally reviewed by a US fellowship trained emergency medicine faculty member. Images will be obtained during scan shifts during which students will scan appropriate patients and review images with the faculty member onsite and independently. Students will attend Bedside US didactics offered by an US EM faculty member every Thursday morning, followed by quality assessment (QA) review of Ultrasound scans performed in the ED. Students will have access and be expected to participate in online and computer-based learning that will be provided for them as well. This clerkship requires prior approval by Clerkship Director. Contact clerkship director Dr. Sally Gragliaat at sally.graglia@stanford.edu and clerkship coordinator Lauren Berriatua at laurenbe@stanford.edu. For visiting international students, please submit your application directly through the International Visiting Student (IVS) Program via https://med.stanford.edu/clerkships/international.html. Please contact visiting-MD-students@stanford.edu if you have any additional questions regarding the IVS Program. PREREQUISITES: Medicine and Surgery rotations, MS 4/5 given preference over MS3. PERIODS AVAILABLE: 6-11, full time for 4 weeks, 2 students per period (subject to change by period). CLERKSHIP DIRECTOR: Sally Graglia, M.D., sally.graglia@stanford.edu. CLERKSHIP COORDINATOR: Lauren Berriatua, laurenbe@stanford.edu. REPORTING INSTRUCTIONS: Where: TBA; Time: 9:00 am. CALL CODE: 0. OTHER FACULTY: K. Anderson, Y. Duanmu, V. Lobo, M. Askar, C. Hill. LOCATION: SUMC.