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41 - 50 of 73 results for: EE

EE 290D: Curricular Practical Training for Electrical Engineers

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.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Tobagi, F. (PI)

EE 290E: Curricular Practical Training for Electrical Engineers

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.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Tobagi, F. (PI)

EE 290F: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290E and instructor consent.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Tobagi, F. (PI)

EE 290G: Curricular Practical Training for Electrical Engineers

For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: EE PhD degree candidacy, an "S" grade in EE 290F and instructor consent.
Terms: Aut, Win, Spr, Sum | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Tobagi, F. (PI)

EE 292A: Electronic Design Automation (EDA) and Machine Learning Hardware

The class teaches cutting-edge optimization and analysis algorithms for the design of complex digital integrated circuits and their use in designing machine learning hardware. It provides working knowledge of the key technologies in Electronic Design Automation (EDA), focusing on synthesis, placement and routing algorithms that perform the major transformations between levels of abstraction and get a design ready to be fabricated. As an example, the design of a convolutional neural network (CNN) for basic image recognition illustrates the interaction between hardware and software for machine learning. It will be implemented on a state-of-the-art FPGA board. Prerequisite: EE 108.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

EE 292D: Machine Learning on Embedded Systems

This is a project-based class where students will learn how to build modern connected embedded systems that utilize machine learning. The class briefly covers how to create embedded systems to cloud services and dives into details of how to do edge and cloud-based machine learning on time-series and image data.nIn the final project students are expected to produce a working system that improves the state of machine learning on the edge or solves a useful task by combining many different connected components. Prerequisites: CS 107(required), CS 229 (recommended), CS 230 (recommended).
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)

EE 292E: Seminar Series for Image Systems Engineering

Seminar. For engineering students interested in camera and display engineering, computer vision, and computational imaging. Speakers include Stanford faculty and research scientists as well as industry professionals, mostly from consumer electronics companies.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

EE 292I: Insanely Great Products: How do they get built?

Great products emerge from a sometimes conflict-laden process of collaboration between different functions within companies. This Seminar seeks to demystify this process via case-studies of successful products and companies. Engineering management and businesspeople will share their experiences in discussion with students. Previous companies profiled: Apple, Intel, Facebook, and Genentech -- to name a few. Previous guests include: Jon Rubinstein (NeXT, Apple, Palm), Diane Greene (VMware), and Ted Hoff (Intel). Pre-requisites: None
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Obershaw, D. (PI)

EE 292T: SmartGrids and Advanced Power Systems Seminar (CEE 272T)

A series of seminar and lectures focused on power engineering. Renowned researchers from universities and national labs will deliver bi-weekly seminars on the state of the art of power system engineering. Seminar topics may include: power system analysis and simulation, control and stability, new market mechanisms, computation challenges and solutions, detection and estimation, and the role of communications in the grid. The instructors will cover relevant background materials in the in-between weeks. The seminars are planned to continue throughout the next academic year, so the course may be repeated for credit.
Terms: Aut, Win, Spr | Units: 1-2 | Repeatable for credit | Grading: Satisfactory/No Credit

EE 300: Master's Thesis and Thesis Research

Independent work under the direction of a department faculty. Written thesis required for final letter grade. The continuing grade 'N' is given in quarters prior to thesis submission. See 390 if a letter grade is not appropriate. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter (ABCD/NP)
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