Short Programs

Data and Models in Engineering, Science and Business [12.156s]

Date: July 23-27, 2012 | Tuition: $3,500 | Continuing Education Units (CEUs): 2.8
*This course has limited enrollment. Apply early to guarantee your spot.
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Overview  |  Learning Objectives  |  Course Materials  |  Who Should Attend  |  Schedule  | 
Participants' Comments  |  Instructors  |  Location  |  Updates

Overview

A course for anyone wanting to fit data to models. Topics include linear least squares, non-linear least squares, singular value decomposition, sensitivity analysis, experiment design, parameter error estimation (Jackknife), grid search, random (Monte-Carlo) search, simulated annealing, genetic algorithms, neural networks and parameter error estimation. Introduces principles leading to rapid application of methods. Includes pre-programmed computer exercises.

Learning Objectives

• Examine how to fit data to models.
• Define linear least squares, non-linear least squares, singular value decomposition, sensitivity analysis, experiment design, and parameter error estimation.
• Appreciate grid search, random search, simulated annealing, genetic algorithms, neural networks, and parameter error estimation.
• Investigate principles leading to rapid application of methods.
• Evaluate the results of pre-programmed computer exercises.

Course Materials

Lectures will be accompanied by copies of all presented material and additional published reviews. Participants are encouraged to study a basic text prior to attendance. Two suggestions are:

Data Reduction and Error Analysis for the Physical Sciences, P. R. Bevington and D. K. Robinson, McGraw-Hill, Inc., 2nd ed., 1992.

Applied Regression Analysis, N. R. Draper and H. Smith, John Wiley and Sons, Inc., 2nd ed., 1981.

Who Should Attend

Anyone who fits data to models. This course is truly broad-based and participants from vastly differing fields are envisioned and encouraged to attend. Some of these fields are engineering, business, natural sciences, geoscience, medicine, statistics, and economics.

Familiarity with computing and statistics is desirable. A fair background in linear algebra is highly recommended.

The course is a condensed version of a regular Fall MIT class with the same title, taught by Professor Morgan. The course has also been given at NASA, the University of the West Indies in Barbados, Sakarya University inTurkey, Stanford University, and Texas A & M University. The Data and Models course has been offered successfully for over 15 years.

Recent and past participants in this course have come from: Air Force Office of Scientific Research (AFOSR), Amgen Inc., AT&T, BAE Systems, Bank of America, Boeing, Boehringer Ingelheim Pharmaceuticals, BP America, Cox Communications, Delphi, Dupont, Environmental Protection Agency, ExxonMobil Chemical, General Motors, Hitachi (Japan), Intel, Johnson & Johnson, Korea Power Co., Kraft Foods, Los Alamos Labs, Mathworks, Mayo Clinic, Merck & Co Inc, Motorola, Naval Research Laboratory, NTT (Japan), Nokia Research Center, Phillips Exeter Academy, Pioneer Investments, Polaroid Corporation, Sandia National Labs, Saudi Arabian Monetary Agency (Saudi Arabia), University of Pennsylvania, University of West Indies (West Indies), US Air Force.

Course schedule and registration times

Class runs 9:00 am - 5:00 pm every day except for Friday when it ends at 1:00 pm.

Registration is on Monday morning from 8:15 - 8:45 am.

Participants' Comments

Deputy Chief Scientist, Air Force Office of Scientific Research (AFOSR)
“The course efficiently provided a broad understanding of a wide variety of methods to a very varied and interesting group of students.”

Associate Professor, University of the Pacific
“Course was well designed. Lab work was very helpful. Application to real-world problems was well illustrated.”

Electrical & Controls Engineer, BP America
I enjoyed the courses taken at MIT this summer. They combined a large amount of theory with lab work in an accelerated fashion. These courses have been the best post-bachelors courses I have taken thus far."

Postdoctoral Research Fellow, Brigham and Women's Hospital
“I found it to be a very stimulating and exciting environment. I felt that the instructors were very knowledgeable in the area and were willing to discuss issues related to applications beyond the classroom. Overall, I would attend courses at the MIT Professional Education - Short Programs in the future and would recommend the program to colleagues.”

Senior Mechanical Engineer, BAE Systems
“The lab portions of the class were thoughtfully planned and very instructive.”

Program Manager, University of Arkansas for Medical Sciences
“The instructors were excellent, and the in-lab reviews with other participants were enlightening.”

Instructors

Frank Dale Morgan obtained his BSc (Math/Physics, 1970) and his MSc (Theoretical Solid State Physics, 1972) from the University of the West Indies, Trinidad, where he was a Lecturer in Physics from 1970-1975. From 1975 to 1981, he completed a PhD in Geophysics at the Massachusetts Institute of Technology. He returned to the University of the West Indies, Trinidad, as a Research Fellow in the Seismic Research Unit. From 1983 to 1985 he was a Research Associate in the Geophysics Department at Stanford University. In 1985 he joined the faculty of the Geophysics Department at Texas A&M University. He is now a Professor of Geophysics at the Massachusetts Institute of Technology in the Department of Earth, Atmospheric, and Planetary Sciences and associated with the Earth Resources Laboratory. His current interests are in rock physics, geoelectromagnetism, applied seismology, inverse theory, environmental and engineering geophysics, electrochemistry, and electronic instrumentation. He teaches courses on the physics and chemistry of rocks, environmental and engineering geophysics, alternative energy, and inverse theory. He is the organizer and principal instructor for the course.

Rama Rao is Head of Global Risk Policy Analytics at Paypal, an eBay Company.

Darrell Coles obtained his BA in Pure Mathematics from the University of Rochester (1994) and his MSc in Geosystems (1998) and PhD in Geophysics (2008) from the Massachusetts Institute of Technology. He completed a joint postdoctoral fellowship with Total E&P and the University of Edinburgh in 2010. Since 2010, he has worked as a research scientist at Schlumberger. His current research interests are in optimal experimental design, inverse and optimization theory, reservoir geophysics, and uncertainty characterization and control.

Location

This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please contact the Short Programs office for further details.

Updates

There are no updates at this time.