2017 COMPUTATION CAMPUS DAYS SCHEDULE

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1 RECOMMENDED COURSE LIST FOR CLASS VISITS 2017 COMPUTATION MEETING WITH DEPARTMENT CHAIR OF ANTROPOLOGY William Mazzarella Wednesday 9:30 a.m. 10:30 a.m., Saieh 242 MATH Analysis In Rn-3, Instructor: Marco Mendez Guaraco Wednesday & Friday 10:30 a.m. -11:20 a.m., Eckhart 308 For students concentrating in Computational Economics with excellent exposure to Real Analysis. This course covers integration in R^n including Fubini's Theorem and iterated integration, line and surface integrals, differential forms, and the theorems of Green, Gauss, and Stokes. MACS Perspectives on Computational Research Instructors: Richard Evans and Benjamin Soltoff Wednesday 11:30 a.m. - 12:50 p.m., Saieh 247 This course focuses on applying computational methods to conducting social scientific research through a student-developed research project. Students will identify a research question of their own interest, collect data, develop, apply, and interpret statistical learning models, and generate a fully reproducible research paper. We will identify how computational methods can be used throughout the research process, from data collection and tidying, to exploration, visualization and modeling, to the final communication of results. The course will include modules on theoretical and practical considerations, including topics such as epistemological questions about research design, identifying data sources, and IRB review. MATH Analysis in Rn-1, Instructor: Daniil Rudenko Wednesday & Friday 11:30 a.m. - 12:20 p.m., Eckhart Hall 202 For students concentrating in Computational Economics with no prior exposure to Real Analysis. Both theoretical and problem solving aspects of multivariable calculus are treated carefully. This course covers the construction of the real numbers, the topology of R^n including the Bolzano- Weierstrass and Heine-Borel theorems, and a detailed treatment of abstract metric spaces, including convergence and completeness, compact sets, continuous mappings, and more. MATH Analysis in Rn 2, Instructor: Marco Mendez Guaraco Wednesday & Friday 11:30 a.m. - 12:20 p.m., Eckhart 308 For students concentrating in Computational Economics who have taken MATH or who have prior exposure to Real Analysis. This course covers differentiation in R^n including partial derivatives, gradients, the total derivative, the Chain Rule, optimization problems, vector-valued functions, and the Inverse and Implicit Function Theorems. MACS Data Visualization, Instructor: Benjamin Soltoff Wednesday 1:30 p.m. - 2:50 p.m., Saieh Hall 247 Social scientists frequently wish to convey information to a broader audience in a cohesive and interpretable manner. Visualizations are an excellent method to summarize information and report analysis and conclusions in a compelling format. This course introduces the theory and applications of data visualization. Students will learn techniques and methods for developing rich, informative and interactive, web-facing visualizations based on principles from graphic design and perceptual psychology. Students will practice these techniques on many types of social science data, including multivariate, temporal, geospatial, text, hierarchical, and network data. These techniques will be developed using a variety of software implementations such as R, ggplot2, D3, and Tableau.

2 MACS Spatial Regression Analysis, Instructor: Luc Anselin Wednesday 1:30 p.m. - 2:50 p.m., Saieh Hall 203 This course covers statistical and econometric methods specifically geared to the problems of spatial dependence and spatial heterogeneity in cross-sectional data. The main objective of the course is to gain insight into the scope of spatial regression methods, to be able to apply them in an empirical setting, and to properly interpret the results of spatial regression analysis. While the focus is on spatial aspects, the types of methods covered have general validity in statistical practice. The course covers the specification of spatial regression models in order to incorporate spatial dependence and spatial heterogeneity, as well as different estimation methods and specification tests to detect the presence of spatial autocorrelation and spatial heterogeneity. Special attention is paid to the application to spatial models of generic statistical paradigms, such as Maximum Likelihood, Generalized Methods of Moments and the Bayesian perspective. An important aspect of the course is the application of open source software tools such as R, GeoDa and PySal to solve empirical problems. CPNS Modeling and Signal Analysis for Neuroscientists Instructor: Wim Van Drongelen Wednesday 1:30 p.m. - 2:50 p.m., BioSci Learning Center 401 The course provides an introduction into signal analysis and modeling for neuroscientists. We cover linear and nonlinear techniques and model both single neurons and neuronal networks. The goal is to provide students with the mathematical background to understand the literature in this field, the principles of analysis and simulation software, and allow them to construct their own tools. Several of the 90-minute lectures include demonstrations and/or exercises in Matlab. MEETING WITH DEPARTMENT CHAIR OF SOCIOLOGY Karin Knorr Cetina Wednesday 3:30 p.m. 4:30 p.m., Social Sciences Building 305 STAT Statistical Theory and Methods 2, Instructor: Chao Gao Thursday 9:00 a.m. - 10:20 a.m., Eckhart 133 This course is the second quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course continues from either STAT or STAT and covers statistical methodology, including the analysis of variance, regression, correlation, and some multivariate analysis. Some principles of data analysis are introduced, and an attempt is made to present the analysis of variance and regression in a unified framework. Statistical software is used. CAPP Machine Learning for Public Policy, Instructor: Rayid Ghani Thursday 10:30 a.m. - 11:50 a.m., 5555 S. Ellis, Room 302 This course will be an introduction to machine learning and how it can be applied to public policy problems. It s designed for students who are interested in learning how to use modern, scalable, computational data analysis methods and tools, and apply them to social and policy problems. This course will teach students: what role machine learning can play in designing, implementing, evaluating, and improving public policy; machine Learning methods and tools; how to solve policy problems using machine learning methods and tools. This is a hands-on course where students will be expected to use Python (as well as other computational tools) to implement solutions to various policy problems. We will cover supervised and unsupervised learning algorithms and will learn how to use them with data from a variety of public policy problems in areas such as education, public health, sustainability, economic development, and public safety.

3 MEETING WITH DEPARTMENT CHAIR OF HISTORY Emilio Kouri Thursday 1:00 p.m. 2:00 p.m., Social Sciences Building 224 CMSC Machine Learning and Large Scale Data Analysis, Instructor: John Lafferty Thursday 1:30 p.m. - 2:50 p.m., Ryerson 251 This course is an introduction to machine learning and the analysis of large data sets using distributed computation and storage infrastructure. Basic machine learning methodology and relevant statistical theory will be presented in lectures. Homework exercises will give students hands-on experience with the methods on different types of data. Methods include algorithms for clustering, binary classification, and hierarchical Bayesian modeling. Data types include images, archives of scientific articles, online ad clickthrough logs, and public records of the City of Chicago. Programming will be based on Python and R, but previous exposure to these languages is not assumed. CAPP Databases for Public Policy, Instructor: Aaron Elmore Thursday 1:30 a.m. - 2:50 p.m., Ryerson 276 The course will cover the foundations of Database Management Systems (DBMS). This includes data models, database design, SQL, core database system components (e.g. transactions, recovery, query processing), distributed databases, NewSQL/NoSQL, and systems for data analytics (e.g. columnorientated databases, data warehouses). The goals for this class are for you to have the ability to model and design a database, an understanding of the core components of a database management system, the ability to write SQL, and an understanding of the differences between databases and data models. MEETING WITH DEPARTMENT CHAIR OF POLITICAL SCIENCE Will Howell Thursday 2:15 p.m. 3:15 p.m., Foster Hall 505 CMSC Machine Learning, Instructor: Imre Kondor Thursday 3:00 p.m. - 4:20 p.m., Hinds 101 This course provides hands-on experience with a range of contemporary machine learning algorithms, as well as an introduction to the theoretical aspects of the subject. Topics covered include: the PAC framework, Bayesian learning, graphical models, clustering, dimensionality reduction, kernel methods including SVMs, matrix completion, neural networks, and an introduction to statistical learning theory. STAT Optimization, Instructor: Lek-Heng Lim Thursday 3:00 p.m. - 4:20 p.m., Eckhart 133 This is an introductory course on optimization that will cover the rudiments of unconstrained and constrained optimization of a real-valued multivariate function. The focus is on the settings where this function is, respectively, linear, quadratic, convex, or differentiable. Time permitting, topics such as nonsmooth, integer, vector, and dynamic optimization may be briefly addressed. Materials will include basic duality theory, optimality conditions, and intractability results, as well as algorithms and applications. PSYC Computational Approaches to Cognitive Neuroscience Instructor: Nicholas Hatsopoulos Thursday 3:30-4:50 p.m., BioSci Learning Center 240 This course is concerned with the relationship of the nervous system to higher order behaviors (e.g., perception, object recognition, action, attention, learning, memory, and decision making). Psychophysical, functional imaging, and electrophysiological methods are introduced. Mathematical

4 and statistical methods (e.g. neural networks and algorithms for studying neural encoding in individual neurons and decoding in populations of neurons) are discussed. Weekly lab sections allow students to program cognitive neuroscientific experiments and simulations. MACS Computational Social Science Workshop, Instructor: James Evans Thursday 5:00 p.m. - 6:30 p.m., Saieh 247 High performance and cloud computing, massive digital traces of human behavior from ubiquitous sensors, and a growing suite of efficient model estimation, machine learning and simulation tools are not just extending classical social science inquiry, but transforming it to pose novel questions at larger and smaller scales. The Computational Social Science (CSS) Workshop is a weekly event that features this work, highlights associated skills and data, and explores the use of CSS in the world. The CSS Workshop alternates weekly between research workshops and professional workshops. The research workshops feature new CSS work from top faculty and advanced graduate students from UChicago and around the world, while professional workshops highlight useful skills and data (e.g., machine learning with Python s scikit-learn; the Twitter firehose API) and showcase practitioners using CSS in the government, industry and nonprofit sectors. Each quarter, the CSS Workshop also hosts a distinguished lecture, debate and dinner, and a student conference. MPCS Databases, Instructor: Zachary Freeman Thursday 5:30 p.m. - 8:30 p.m., 5555 S. Ellis 302 Students will learn database design and development and will build a simple but complete web application powered by a relational database. We start by showing how to model relational databases using the prevailing technique for conceptual modeling -- Entity-Relationship Diagrams (ERD). Concepts covered include entity sets and relationships, entity key as a unique identifier for each object in an entity set, one-one, many-one, and many-many relationships as well as translational rules from conceptual modeling (ERD) to relational table definitions. We also examine the relational model and functional dependencies and their application to the methods for improving database design: normal forms and normalization. After design and modeling, students will learn the universal language of relational databases: SQL (Structured Query Language). We start by introducing relational algebra -- the theoretical foundation of SQL. Then we examine in detail the two aspects of SQL: data definition language (DDL) and the data manipulation language (DML). Concepts covered include subqueries (correlated and uncorrelated), aggregation, various types of joins including outer joins and syntax alternatives. MEETING WITH DEPARTMENT CHAIR OF ECONOMICS John List Friday 9:30 a.m. 10:30 a.m., Saieh Hall 112 CAPP Computer Science with Applications-3, Instructor: Matthew Wachs Friday 9:30 a.m. - 10:20 a.m., Cobb Hall 102 This three-quarter sequence teaches computational thinking and skills to students who are majoring in the sciences, mathematics, and economics. Lectures cover topics in (1) programming, such as recursion, abstract data types, and processing data; (2) computer science, such as clustering methods, event-driven simulation, and theory of computation; and to a lesser extent (3) numerical computation, such as approximating functions and their derivatives and integrals, solving systems of linear equations, and simple Monte Carlo techniques. Applications from a wide variety of fields serve both as examples in lectures and as the basis for programming assignments. In recent offerings, students have written programs to evaluate betting strategies, determine the number of machines needed at a polling place, and predict the size of extinct marsupials. Students learn Java, Python, R and C++.

5 MEETING WITH DEPARTMENT CHAIR OF PSYCHOLOGY David Gallo Friday 10:45 a.m. 11:45 a.m., Green Hall 104 MPCS Machine Learning, Instructor: Amitabh Chaudhary Friday 5:30 p.m. - 8:30 p.m., Ryerson 251 This course introduces the fundamental concepts and techniques in data mining, machine learning, and statistical modeling, and the practical know- how to apply them to real-world data through Pythonbased software. The course examines in detail topics in both supervised and unsupervised learning. These include linear and logistic regression and regularization; classi cation using decision trees, nearest neighbors, naive Bayes, boosting, random trees, and arti cial neural networks; clustering using k-means, expectation-maximization, hierarchical approaches, and density-based techniques; and dimensionality reduction through PCA and SVD. Students use Python and Python libraries such as NumPy, SciPy, matplotlib, and pandas for for implementing algorithms and analyzing data.

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