Data and Models in Engineering, Science, and Business
Date: July 21-25, 2014 | Tuition: $3,500 | Continuing Education Units (CEUs): 2.8
*This course has limited enrollment. Apply early to guarantee your spot.
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This course aims to teach a suite of algorithms and concepts to a diverse set of participants interested in the general concept of fitting Data to Models.
Rather than starting with abstract Linear Algebra and staying on a highly mathematical path for most of the course, turning to some computation only towards the end, this course starts with mostly simple computational methods and introduces some more difficult mathematical concepts towards the end. This latter approach provides opportunities for much hands-on learning and participants leave with real practical knowledge of some of the basic algorithms. This method also, by design, fits in with our method of morning lectures and afternoon practice on computers.
This is a very broad course and is intended only to cover the fundamentals of each technique we address. However, the gain is that we can cover many different approaches. Think of it this way: we cover the first chapter or two of a specialized ‘book’ on a given method. We get you through the fundamentals, which allow you to then get further through the book on your own. Another way of thinking of our approach is the analogy of a carpenter’s tools. The goal is for participants to understand the utility of each tool, not to become specialists in any one method. In that sense, the course is introductory and general.
We tap into material from a very wide selection of literature in many disciplines involving computation, including but not limited to: statistics and applied mathematics, science, engineering, medicine and biomedicine, computer science, geosciences, system engineering, economics, insurance, finance, business, and aerospace engineering. More specific areas in which you might come across relevant books are: Regression, Non-linear Regression, Linear and Non-Linear Parameter Estimation, Inversion, System Identification, Econometrics, Biometrics, etc. The diversity of the participants and their fields provides many perspectives on our common interest in Data and Models.
Fundamentals: Core concepts, understandings, and tools (75%)
Latest Developments: Recent advances and future trends (25%)
Lecture: Delivery of material in a lecture format (40%)
Discussion: Guided discussion reinforcing lectures and computer lab work (15%)
Labs: Computer-based participatory learning (45%)
Introductory: Appropriate for a general audience (30%)
Specialized: Assumes experience in practice area or field (50%)
Advanced: In-depth explorations at the graduate level (20%)
- 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.
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 in Turkey, Stanford University, and Texas A&M University.
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, University of Pennsylvania, University of West Indies, US Air Force.
The format of each day is generally the same: mornings are devoted to lectures while participants spend the afternoons running pre-programmed software based on those lectures. During the afternoons, we often stop the class to have a discussion of progress and to give helpful tips. Students can work singly or in pairs at the computer.
Individual lectures will address the following topics:
Philosophy of Data and Models
Straight Line Data Analysis
Levenberg-Marquardt and Ridge Regression Algorithms
Damped Least Squares Comparison
Singular Value Decomposition
Random and Grid-Search Methods
Simulated Annealing and Genetic Algorithms
Parameter Error Estimates
Large Inverse Problems
Note that the order of the lectures can vary from that given above. A bound copy of all Power Point lecture notes is given to each student to follow lectures and make notes. Computers are required. Any PC platform, including Macs running Virtual PC, will be fine. Participants should have administrator privileges on their machines.
Course schedule and registration times
Registration is on Monday morning from 7:30 - 8:00 am.
Class runs 9:00 am to 5:00 pm every day, but participants are welcome to leave early on Friday.
9:00 am - 12:00 pm - Lecture
12:00 pm - 1:00 pm - Lunch Break
1:00 pm - 5:00 pm - Lab Exercises
Deputy Chief Scientist, Air Force Office of Scientific Research
“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.”
engineering specialist, baxter healthcare
“To remain competitive, we need to implement process improvements across the organization to greatly improve efficiency while simultanously increasing the robustness and efficacy of our products. This knowledge provides additional tools to accomplish this.”
director of research and development, prescient ridge management
“Definitely a good balance between the lectures in the morning which gave the theory and the labs in the afternoon which allowed time to work on the practical application.”
simulation consultant, dematic
“The knowledge will help me to better analyze customer data and build more sophisticated simulation models.”
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.
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.
Rama Rao is currently Senior Director and Head of Risk Analytics at PayPal. He leads a team of data analysts who monitor business performance and perform the analytics that go into creating PayPalís risk policies around the world—boundaries within which users can transact and experience PayPal. Rama has held various analytics roles within PayPal over the last five years, and has led several innovations in business analysis and has also helped build out the PayPalís risk analytics function in India. Prior to PayPal, Rama was at MIT for nine years where he led a research program, funded by an international consortium of oil majors and service companies, working on innovative uses of acoustic measurements to image and locate hydrocarbons. During this time, Rama taught a fall graduate course in data analytics along with Prof. Dale Morgan. Rama also spent a year at McKinsey where he worked on client initiatives aimed at creating new businesses that leverage existing assets and innovations. Rama continues to visit MIT every summer to teach this course. Rama completed his undergraduate studies at the Indian Institute of Technology, Madras followed by dual Masters and a PhD at MIT.
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 complete the Custom Programs request form for further details.
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