Download Computation Course Listings

2017 COMPUTATION
CAMPUS DAYS SCHEDULE
RECOMMENDED COURSE LIST FOR CLASS VISITS
MEETING WITH DEPARTMENT CHAIR OF ANTROPOLOGY – William Mazzarella
Wednesday 9:30 a.m. – 10:30 a.m., Saieh 242
MATH 20500 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 30200 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 20300 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 BolzanoWeierstrass and Heine-Borel theorems, and a detailed treatment of abstract metric spaces, including
convergence and completeness, compact sets, continuous mappings, and more.
MATH 20400 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 20300 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 40700 - 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.
2017 COMPUTATION
CAMPUS DAYS SCHEDULE
MACS 55000 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 32111 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 24500 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 24400 or STAT 24410 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 30254 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.
2017 COMPUTATION
CAMPUS DAYS SCHEDULE
MEETING WITH DEPARTMENT CHAIR OF HISTORY – Emilio Kouri
Thursday 1:00 p.m. – 2:00 p.m., Social Sciences Building 224
CMSC 25025 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 30235 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 35400 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 28000 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 34410 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
2017 COMPUTATION
CAMPUS DAYS SCHEDULE
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 50000 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 53001 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 30123 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++.
2017 COMPUTATION
CAMPUS DAYS SCHEDULE
MEETING WITH DEPARTMENT CHAIR OF PSYCHOLOGY – David Gallo
Friday 10:45 a.m. – 11:45 a.m., Green Hall 104
MPCS 53111 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.