EE 65: Modern Physics for Engineers
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). Prerequisite:
Physics 41. Pre or corequisite:
Math 53 or
CME 102.
Terms: Spr

Units: 4

UG Reqs: GER: DBNatSci, GER:DBEngrAppSci, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Heinz, T. (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:
CME102.
Terms: Spr

Units: 4

UG Reqs: GER:DBEngrAppSci, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Murmann, B. (PI)
;
Wong, S. (PI)
EE 102B: Signal Processing and Linear Systems II
Continuation of
EE 102A. Concepts and tools for continuous and discretetime 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: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Goldsmith, A. (PI)
EE 103: Introduction to Matrix Methods (CME 103)
Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the kmeans algorithm. Matrices, left and right inverses, QR factorization. Leastsquares and model fitting, regularization and crossvalidation. Constrained and nonlinear leastsquares. Applications include timeseries prediction, tomography, optimal control, and portfolio optimization. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
MATH 51 or
CME 100, and basic knowledge of computing (
CS 106A is more than enough, and can be taken concurrently).
EE103/CME103 and
Math 104 cover complementary topics in applied linear algebra. The focus of EE103 is on a few linear algebra concepts, and many applications; the focus of
Math 104 is on algorithms and concepts.
Terms: Aut, Spr

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Osgood, B. (PI)
;
Tse, D. (PI)
;
Angeris, G. (TA)
;
Chang, S. (TA)
;
Daniel, J. (TA)
;
Degleris, A. (TA)
;
Go, C. (TA)
;
Jani, T. (TA)
;
Jimenez, S. (TA)
;
Kamath, G. (TA)
;
Li, L. (TA)
;
Lin, J. (TA)
;
Nishimura, M. (TA)
;
Patel, N. (TA)
;
Pathak, R. (TA)
;
Sholar, J. (TA)
;
Spear, L. (TA)
EE 104: Introduction to Machine Learning
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. In this initial offering, enrollment is limited to 50 students. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:
EE 103;
EE 178 or
CS 109; CS106A or equivalent.
Terms: Spr

Units: 35

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
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

Grading: Letter or Credit/No Credit
Instructors:
Olukotun, O. (PI)
EE 116: Semiconductor Device Physics
The fundamental operation of semiconductor devices and overview of applications. The physical principles of semiconductors, both silicon and compound materials; operating principles and device equations for junction devices (diodes, bipolar transistor, photodetectors). Introduction to quantum effects and band theory of solids. Recommended corequisites:
EE 65 and
EE 101B. NonEE majors are encouraged to take
ENGR 40 before
EE 116.
Terms: Spr

Units: 3

UG Reqs: GER:DBEngrAppSci

Grading: Letter or Credit/No Credit
Instructors:
Lu, C. (PI)
EE 134: Introduction to Photonics
Photonics, optical components, and fiber optics. Conceptual and mathematical tools for design and analysis of optical communication, sensor and imaging systems. Experimental characterization of semiconductor lasers, optical fibers, photodetectors, receiver circuitry, fiber optic links, optical amplifiers, and optical sensors. Class project on confocal microscopy or other method of sensing or analyzing biometric data. Laboratory experiments. Prerequisite:
EE 102A and one of the following:
EE 42,
Physics 43, or
Physics 63.
Terms: Spr

Units: 4

UG Reqs: GER:DBEngrAppSci

Grading: Letter (ABCD/NP)
Instructors:
Bowden, A. (PI)
EE 178: Probabilistic Systems Analysis
Introduction to probability and statistics and their role in modeling and analyzing real world phenomena. Events, sample space, and probability. Discrete random variables, probability mass functions, independence and conditional probability, expectation and conditional expectation. Continuous random variables, probability density functions, independence and expectation, derived densities. Transforms, moments, sums of independent random variables. Simple random processes. Limit theorems. Introduction to statistics: significance, estimation and detection. Prerequisites: basic calculus.
Terms: Aut, Spr

Units: 4

UG Reqs: GER:DBEngrAppSci

Grading: Letter or Credit/No Credit
EE 190: Special Studies or Projects in Electrical Engineering
Independent work under the direction of a faculty member. Individual or team activities involve lab experimentation, design of devices or systems, or directed reading. Course may be repeated for credit.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Allison, D. (PI)
;
Arbabian, A. (PI)
;
Bambos, N. (PI)
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Instructors:
Allison, D. (PI)
;
Arbabian, A. (PI)
;
Bambos, N. (PI)
;
Boahen, K. (PI)
;
Boneh, D. (PI)
;
Bowden, A. (PI)
;
Boyd, S. (PI)
;
Bube, R. (PI)
;
Cioffi, J. (PI)
;
DaRosa, A. (PI)
;
Dally, B. (PI)
;
Dasher, R. (PI)
;
Duchi, J. (PI)
;
Dutton, R. (PI)
;
El Gamal, A. (PI)
;
EmamiNaeini, A. (PI)
;
Engler, D. (PI)
;
Fan, J. (PI)
;
Fan, S. (PI)
;
Franklin, G. (PI)
;
FraserSmith, A. (PI)
;
GarciaMolina, H. (PI)
;
Gibbons, J. (PI)
;
Gill, J. (PI)
;
Giovangrandi, L. (PI)
;
Girod, B. (PI)
;
Goldsmith, A. (PI)
;
Goodman, J. (PI)
;
Gray, R. (PI)
;
Hanrahan, P. (PI)
;
Harris, J. (PI)
;
Harris, S. (PI)
;
Hennessy, J. (PI)
;
Hesselink, L. (PI)
;
Horowitz, M. (PI)
;
Howe, R. (PI)
;
Inan, U. (PI)
;
Kahn, J. (PI)
;
Katti, S. (PI)
;
Kazovsky, L. (PI)
;
KhuriYakub, B. (PI)
;
Kino, G. (PI)
;
Kovacs, G. (PI)
;
Kozyrakis, C. (PI)
;
Lall, S. (PI)
;
Lee, T. (PI)
;
Levin, C. (PI)
;
Levis, P. (PI)
;
Levoy, M. (PI)
;
McCluskey, E. (PI)
;
McKeown, N. (PI)
;
Meng, T. (PI)
;
Miller, D. (PI)
;
Mitchell, J. (PI)
;
Mitra, S. (PI)
;
Montanari, A. (PI)
;
Murmann, B. (PI)
;
Nishi, Y. (PI)
;
Nishimura, D. (PI)
;
Olukotun, O. (PI)
;
Osgood, B. (PI)
;
Paulraj, A. (PI)
;
Pauly, J. (PI)
;
Pease, R. (PI)
;
Pianetta, P. (PI)
;
Plummer, J. (PI)
;
Poon, A. (PI)
;
Pop, E. (PI)
;
Prabhakar, B. (PI)
;
RivasDavila, J. (PI)
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Rosenblum, M. (PI)
;
Saraswat, K. (PI)
;
Shen, Z. (PI)
;
Shenoy, K. (PI)
;
Solgaard, O. (PI)
;
Spielman, D. (PI)
;
Thompson, N. (PI)
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Thrun, S. (PI)
;
Tobagi, F. (PI)
;
Tyler, G. (PI)
;
Van Roy, B. (PI)
;
Vuckovic, J. (PI)
;
Wang, S. (PI)
;
Weissman, T. (PI)
;
Wenstrand, J. (PI)
;
Wetzstein, G. (PI)
;
Widom, J. (PI)
;
Widrow, B. (PI)
;
Wong, H. (PI)
;
Wong, S. (PI)
;
Wooley, B. (PI)
;
Wootters, M. (PI)
;
Yamamoto, Y. (PI)
;
Zebker, H. (PI)