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 in the computer laboratory.
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.
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 (Saudi Arabia), University of Pennsylvania, University of West Indies (West Indies), US Air Force.
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