2018-2019 2019-2020 2020-2021 2021-2022 2022-2023
by subject...

1 - 10 of 62 results for: EE ; Currently searching spring courses. You can expand your search to include all quarters

EE 12Q: Science, Technology, Art

This course presents the interwoven histories of science, technology, and art starting in the late Medieval period in Europe, through the Renaissance, up to the Modern era. It explores how advances in science and technology were exploited by artists and how problems confronted by artists were often solved by scientists and technologists, to the advancement of all. Topics include the geometry of perspective, optics of image making, chemistry of pigments and dyes, and the role of computing in art. A subsidiary theme is how artists indirectly interpreted scientific discoveries (telescope views of the heavens, microscope views of the teeny, Theory of Relativity, ...). Whenever possible, the technical evidence, developments, and of course art will be presented visually in the class.
Terms: Spr | Units: 3 | Repeatable 1 times (up to 3 units total)
Instructors: Stork, D. (PI)

EE 42: Introduction to Electromagnetics and Its Applications (ENGR 42)

Electricity and magnetism and its essential role in modern electrical engineering devices and systems, such as sensors, displays, DVD players, and optical communication systems. The topics that will be covered include electrostatics, magnetostatics, Maxwell's equations, one-dimensional wave equation, electromagnetic waves, transmission lines, and one-dimensional resonators. Pre-requisites: none.
Terms: Spr, Sum | Units: 5 | UG Reqs: GER:DB-EngrAppSci, WAY-SMA, WAY-AQR
Instructors: Solgaard, O. (PI)

EE 65: Modern Physics for Engineers (ENGR 65)

This course introduces the core ideas of modern physics that enable applications ranging from solar energy and efficient lighting to the modern electronic and optical devices and nanotechnologies that sense, process, store, communicate and display all our information. Though the ideas have broad impact, the course is widely accessible to engineering and science students with only basic linear algebra and calculus through simple ordinary differential equations as mathematics background. Topics include the quantum mechanics of electrons and photons (Schrödinger's equation, atoms, electrons, energy levels and energy bands; absorption and emission of photons; quantum confinement in nanostructures), the statistical mechanics of particles (entropy, the Boltzmann factor, thermal distributions), the thermodynamics of light (thermal radiation, limits to light concentration, spontaneous and stimulated emission), and the physics of information (Maxwell's demon, reversibility, entropy and noise in physics and information theory). Pre-requisite: Physics 41. Pre- or co-requisite: Math 53 or CME 102.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, GER:DB-EngrAppSci, WAY-SMA
Instructors: Congreve, D. (PI)

EE 101B: Circuits II

Continuation of EE101A. Introduction to circuit design for modern electronic systems. Modeling and analysis of analog gain stages, frequency response, feedback. Filtering and analog to digital conversion. Fundamentals of circuit simulation. Prerequisites: EE101A, EE102A. Recommended: MATH 53 or CME102.
Terms: Spr | Units: 4 | UG Reqs: WAY-SMA, GER:DB-EngrAppSci
Instructors: Murmann, B. (PI)

EE 102B: Signal Processing and Linear Systems II

Continuation of EE 102A. Concepts and tools for continuous- and discrete-time signal and system analysis with applications in communications, signal processing and control. Analog and digital modulation and demodulation. Sampling, reconstruction, decimation and interpolation. Finite impulse response filter design. Discrete Fourier transforms, applications in convolution and spectral analysis. Laplace transforms, applications in circuits and feedback control. Z transforms, applications in infinite impulse response filter design. Prerequisite: EE 102A.
Terms: Spr | Units: 4 | UG Reqs: WAY-FR, GER:DB-EngrAppSci, WAY-AQR

EE 104: Introduction to Machine Learning (CME 107)

Introduction to machine learning. Formulation of supervised and unsupervised learning problems. Regression and classification. Data standardization and feature engineering. Loss function selection and its effect on learning. Regularization and its role in controlling complexity. Validation and overfitting. Robustness to outliers. Simple numerical implementation. Experiments on data from a wide variety of engineering and other disciplines. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites: ENGR 108; EE 178 or CS 109; CS106A or equivalent.
Terms: Spr | Units: 3-5
Instructors: Lall, S. (PI)

EE 109: Digital Systems Design Lab

The design of integrated digital systems encompassing both customized software and hardware. Software/hardware design tradeoffs. Algorithm design for pipelining and parallelism. System latency and throughput tradeoffs. FPGA optimization techniques. Integration with external systems and smart devices. Firmware configuration and embedded system considerations. Enrollment limited to 25; preference to graduating seniors. Prerequisites: 108B, and CS 106B or X.
Terms: Spr | Units: 4
Instructors: Olukotun, O. (PI)

EE 116: Semiconductor Devices for Energy and Electronics

The underpinnings of modern technology are the transistor (circuits), the capacitor (memory), and the solar cell (energy). EE 116 introduces the physics of their operation, their historical origins (including Nobel prize breakthroughs), and how they can be optimized for future applications. The class covers physical principles of semiconductors, including silicon and new material discoveries, quantum effects, band theory, operating principles, and device equations. Recommended (but not required) co-requisite: EE 65 or equivalent.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-SMA, WAY-FR

EE 157: Electric Motors for Renewable Energy, Robotics, and Electric Vehicles

An introduction to electric motors and the principles of electromechanical energy conversion. Students will learn about, design, and build an electric motor system, choosing from one of three application areas: renewable energy (wind turbines), robotics (drones and precision manufacturing), or electric vehicles (cars, ships, and airplanes). Topics covered include ac and dc rotating machines, power electronics inverters and drives, and control techniques. Prerequisite: EE 42, Physics 43, ENGR 40M or equivalent.
Terms: Spr | Units: 3
Instructors: Clark, S. (PI)

EE 178: Probabilistic Systems Analysis

Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals. Prerequisites: Calculus at the level of MATH 51, CME 100 or equivalent and basic knowledge of computing at the level of CS106A.
Terms: Aut, Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
Filter Results:
term offered
updating results...
teaching presence
updating results...
number of units
updating results...
time offered
updating results...
updating results...
UG Requirements (GERs)
updating results...
updating results...
updating results...
© Stanford University | Terms of Use | Copyright Complaints