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## 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 k-means algorithm. Matrices, left and right inverses, QR factorization. Least-squares and model fitting, regularization and cross-validation. Constrained and nonlinear least-squares. Applications include time-series 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, Win, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

## 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, non-parametric 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:DB-Math, WAY-AQR, WAY-FR

## CME 108:Introduction to Scientific Computing (MATH 114)

Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floating-point 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:DB-EngrAppSci, WAY-AQR, WAY-FR

## COMM 138:Deliberative Democracy Practicum: Applying Deliberative Polling (COMM 238)

In this course, students will work directly on a real-world deliberative democracy project using the method of Deliberative Polling. Students in this course will work in partnership with the Center for Deliberative Democracy at Stanford, a research center devoted to the research in democracy and public opinion around the world. This unique practicum will allow students to work on an actual Deliberative Polling project on campus. In just one quarter, the students will prepare for, implement, and analyze the results for an Deliberative Polling project. This is a unique opportunity that allows students to take part in the entire process of a deliberative democracy project. Through this practicum, students will learn and apply quantitative and qualitative research methods. Students will explore the underlying challenges and complexities of what it means to actually do community-engaged research in the real world. As such, this course will provide students with skills and experience in rese more »
In this course, students will work directly on a real-world deliberative democracy project using the method of Deliberative Polling. Students in this course will work in partnership with the Center for Deliberative Democracy at Stanford, a research center devoted to the research in democracy and public opinion around the world. This unique practicum will allow students to work on an actual Deliberative Polling project on campus. In just one quarter, the students will prepare for, implement, and analyze the results for an Deliberative Polling project. This is a unique opportunity that allows students to take part in the entire process of a deliberative democracy project. Through this practicum, students will learn and apply quantitative and qualitative research methods. Students will explore the underlying challenges and complexities of what it means to actually do community-engaged research in the real world. As such, this course will provide students with skills and experience in research design in deliberative democracy, community and stakeholder engagement, and the practical aspects of working in local communities. This practicum is a collaboration between the Center for Deliberative Democracy and the Haas Center for Public Service. CDD website: http://cdd.stanford.edu; Hass Center website: https://haas.stanford.edu
Terms: Spr | Units: 3-5 | UG Reqs: WAY-AQR, WAY-SI | Repeatable for credit

## COMM 173E:Data Challenge Lab (ENGR 150)

In this lab, students develop the practical skills of data science by solving a series of increasingly difficult, real problems. Skills developed include: data manipulation, data visualization, exploratory data analysis, and basic modeling. The data challenges each student undertakes are based upon their current skills. Students receive one-on-one coaching and see how expert practitioners solve the same challenges. Limited enrollment; application required. See http://datalab.stanford.edu for more information.
Terms: Win, Spr | Units: 3-5 | UG Reqs: WAY-AQR, WAY-CE

## CS 102:Working with Data - Tools and Techniques

Aimed at non-CS undergraduate and graduate students who want to learn a variety of tools and techniques for working with data. 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 data sets. This course provides a broad and practical introduction to working with data: data analysis techniques including databases, data mining, machine learning, and data visualization; data analysis tools including spreadsheets, Tableau, relational databases and SQL, Python, and R; introduction to network analysis and unstructured data. Tools and techniques are hands-on 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: Spr | Units: 3-4 | UG Reqs: WAY-AQR

## 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, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

## CS 124:From Languages to Information (LINGUIST 180, LINGUIST 280)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (string algorithms, edit distance, language modeling, machine learning classifiers, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS103, CS107, CS109.
Terms: Win | Units: 3-4 | UG Reqs: WAY-AQR

## 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.
Last offered: Spring 2018 | UG Reqs: WAY-AQR, WAY-SI

## CSRE 180B:Introduction to Data Analysis (SOC 180B, SOC 280B)

Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses.
Terms: Spr | Units: 4 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SI
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