CHEM 134: Analytical Chemistry Laboratory
Classical analysis methods, statistical analyses, chromatography, and spectroscopy will be covered with an emphasis upon quantitative measurements and data analysis. WIM course with full lab reports and oral communication. Concludes with studentdeveloped quantitative project. Prerequisite:
Chem 35
Terms: Spr

Units: 5

UG Reqs: GER: DBNatSci, WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Cox, C. (PI)
;
Dai, H. (PI)
;
Disselkoen, K. (TA)
;
Fica Contreras, S. (TA)
;
Weir, H. (TA)
CHEMENG 20: Introduction to Chemical Engineering (ENGR 20)
Overview of chemical engineering through discussion and engineering analysis of physical and chemical processes. Topics: overall staged separations, material and energy balances, concepts of rate processes, energy and mass transport, and kinetics of chemical reactions. Applications of these concepts to areas of current technological importance: biotechnology, energy, production of chemicals, materials processing, and purification. Prerequisite:
CHEM 31.
Terms: Spr

Units: 4

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Khosla, C. (PI)
;
Arussy, C. (TA)
;
Floyd, C. (TA)
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Instructors:
Khosla, C. (PI)
;
Arussy, C. (TA)
;
Floyd, C. (TA)
;
Gauthier, J. (TA)
;
Santollani, L. (TA)
CHEMENG 60Q: Environmental Regulation and Policy
Preference to sophomores. How environmental policy is formulated in the U.S. How and what type of scientific research is incorporated into decisions. How to determine acceptable risk, the public's right to know of chemical hazards, waste disposal and clean manufacturing, brownfield redevelopment, and new source review regulations. The proper use of science and engineering including media presentation and misrepresentation, public scientific and technical literacy, and emotional reactions. Alternative models to formulation of environmental policy. Political and economic forces, and stakeholder discussions.
Terms: Aut

Units: 3

UG Reqs: GER:DBEngrAppSci, WAYAQR

Grading: Letter (ABCD/NP)
Instructors:
Libicki, S. (PI)
CHEMENG 70Q: Masters of Disaster
Preference to sophomores. For students interested in science, engineering, politics, and the law. Learn from past disasters to avoid future ones. How disasters can be tracked to failures in the design process. The roles of engineers, artisans, politicians, lawyers, and scientists in the design of products. Failure as rooted in oversight in adhering to the design process. Student teams analyze real disasters and design new products presumably free from the potential for disastrous outcomes.
Terms: not given this year, last offered Autumn 2016

Units: 3

UG Reqs: GER:DBEngrAppSci, WAYAQR

Grading: Letter (ABCD/NP)
CME 103: Introduction to Matrix Methods (EE 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)
;
Harvey, B. (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)
;
Prakash, L. (TA)
;
Sholar, J. (TA)
;
Spear, L. (TA)
CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)
Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, nonparametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite:
CME 100/ENGR154 or
MATH 51 or 52.
Terms: Win, Sum

Units: 4

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Amidi, S. (TA)
;
Chhor, G. (TA)
;
Chu, C. (TA)
;
Lakshman, V. (TA)
;
Pyron, J. (TA)
;
Sagastuy Brena, J. (TA)
;
Wu, Y. (TA)
CME 108: Introduction to Scientific Computing (MATH 114)
Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floatingpoint arithmetic, nonlinear equations, numerical solution of systems of algebraic equations, banded matrices, least squares, unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, numerical stability for time dependent problems and stiffness. Implementation of numerical methods in MATLAB programming assignments. Prerequisites:
MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of
CS 106A or higher).
Terms: Win, Sum

Units: 3

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
CS 102: Big Data: Tools and Techniques, Discoveries and Pitfalls
Aimed at nonCS undergraduate and graduate students who want to learn the basics of big data tools and techniques and apply that knowledge in their areas of study. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing massive data sets. At the same time, it is surprisingly easy to make errors or come to false conclusions from data analysis alone. This course provides a broad and practical introduction to big data: data analysis techniques including databases, data mining, and machine learning; data analysis tools including spreadsheets, relational databases and SQL, Python, and R; data visualization techniques and tools; pitfalls in data collection and analysis; historical context, privacy, and other ethical issues. Tools and techniques are handson but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science,
CS106A, or other equivalent programming experience.
Terms: Aut, Spr

Units: 34

UG Reqs: WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Widom, J. (PI)
;
Chen, S. (TA)
;
Haigh, A. (TA)
;
Jang, J. (TA)
;
Kilpatrick, J. (TA)
;
Kunnasagaran, A. (TA)
;
Meister, C. (TA)
;
Min, J. (TA)
;
Pinkerton, R. (TA)
;
Shen, K. (TA)
;
Wang, L. (TA)
CS 109: Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of
MATH 51 or
CME 100 or equivalent.
Terms: Aut, Spr, Sum

Units: 35

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Piech, C. (PI)
;
Sahami, M. (PI)
;
Yan, L. (PI)
;
Banerjee, O. (TA)
;
Chartock, E. (TA)
;
Chen, J. (TA)
;
Chen, W. (TA)
;
Corcoran, B. (TA)
;
Daniel, J. (TA)
;
Dasu, G. (TA)
;
Glaser, N. (TA)
;
Hemmati, S. (TA)
;
Istrate, A. (TA)
;
Johnston, L. (TA)
;
Kim, S. (TA)
;
Le, Y. (TA)
;
Liu, Y. (TA)
;
Moore, D. (TA)
;
Palamuttam, R. (TA)
;
Prasetio, M. (TA)
;
Redondo, E. (TA)
;
Shukla, A. (TA)
;
Ulmer, B. (TA)
;
Wright, D. (TA)
;
Yan, L. (TA)
CSRE 141S: Immigration and Multiculturalism (POLISCI 141A)
What are the economic effects of immigration? Do immigrants assimilate into local culture? What drives native attitudes towards immigrants? Is diversity bad for local economies and societies and which policies work for managing diversity and multiculturalism? We will address these and similar questions by synthesizing the conclusions of a number of empirical studies on immigration and multiculturalism. The emphasis of the course is on the use of research design and statistical techniques that allow us to move beyond correlations and towards causal assessments of the effects of immigration and immigration policy.
Terms: Spr

Units: 5

UG Reqs: WAYAQR, WAYSI

Grading: Letter or Credit/No Credit
Instructors:
Fouka, V. (PI)
;
Mousa, S. (TA)
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