MIT: Independent Activities Period: IAP

IAP 2017

Computing in Optimization and Statistics

Phil Chodrow, Joey Huchette, Brad Sturt, Dimitris Bertsimas, Professor

Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/01
Attendance: Participants welcome at individual sessions

The "big data revolution" has placed added emphasis on computational techniques for
decision-making with data. Large-scale optimization, data analysis and visualization are now
commonplace among researchers and practitioners alike. More than ever, there is a need not
only to develop techniques, but also to implement and use them in computational practice.

This course (formerly “Software Tools for Operations Research”) is a multi-session workshop
on software tools for informing decision-making using data, with a focus on optimization and
statistics. We concentrate on teaching elementary principles of computational practice using
common software and practical methods. By the end of the course, students will possess a
baseline technical knowledge for modern research practice. Class participation and individual
hands-on coding are stressed in each session.

The course is divided into 8 self-contained modules. Each module consists of a 3-hour,
interactive workshop where participants learn a specific software tool. Class participation, group
code-reviews and individual hands-on coding are stressed in each session. At the end of the
module, participants will be able to use the software and techniques learned in their own
research. Participants will also leave each workshop with code they, themselves, have authored
to use for future reference.

Sponsor(s): Operations Research Center
Contact: Brad Sturt,

Module 1

Jan/10 Tue 09:00AM-12:00PM E51-151

Motivation, Terminal, Github

Jackie Baek, Brad Sturt

Module 2

Jan/12 Thu 09:00AM-12:00PM E51-151

Data Wrangling & Visualization in R

Steven Morse, Alex Weinstein

Module 3

Jan/17 Tue 09:00AM-12:00PM E51-151

Statistical Modeling and Machine Learning in R

Colin Pawlowski, Clark Pixton

Module 4

Jan/19 Thu 09:00AM-12:00PM E51-151

Advanced Techniques for Data Science in R

Phil Chodrow

Module 5

Jan/19 Thu 09:00AM-12:00PM E51-151

Introduction to Julia and JuMP, Linear Optimization, and Engaging

Joey Huchette, Sebastien Martin

Module 6

Jan/26 Thu 09:00AM-12:00PM E51-151

Nonlinear and Integer Optimization in JuMP

Miles Lubin, Yee Sian Ng

Module 7

Jan/31 Tue 09:00AM-12:00PM E51-151

Excel for Operations Research

Charles Thraves

Module 8

Feb/02 Thu 09:00AM-12:00PM E51-151

Deep Learning in TensorFlow, Python

Eli Gutin, Brad Sturt