MIT/ILP Calendar Event
Data and Models: Regression Analytics
OverviewThis 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. It starts with mostly simple linear algebra and computational methods, and introduces some more difficult mathematical concepts towards the end. This method also, by design, fits in with our approach of morning lectures and afternoon practice on personal computers. The combined teaching system provides opportunities for much hands-on learning and participants leave the course with practical knowledge of the basic algorithms.
The course is very broad and is primarily intended to cover the fundamentals of each technique we address. Consequently, the major 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 therefore get you through the many fundamentals, which then allow you to dig 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 and not to become specialists in any one method. In that sense the course is introductory and general. The course taps 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 past participants and their fields has always provided many perspectives on our common interest in data and models. Please note that we do not specifically cover non-parametric statistics, principal component analysis, or Big Data.
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 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, University of Science and Technology of China,the Cyprus Institute, and Texas A&M University.
MIT is located in Cambridge, Massachusetts. The campus, situated in close proximity to Boston’s Logan International Airport, profits from Boston’s excellent public transportation system and the on-campus Tech Shuttle. The closest subway station is Kendall Square, which acts as a commercial center for MIT and the local community.
FOR FURTHER INFORMATION OR TO REGISTER, CONTACT:
MIT Professional Education - Short Programs
238 Main Street, Suite 401
Cambridge, MA 02139
TEL: 617 253 2101 * FAX: 617 258 8831
ILP members receive a 15% discount on all MIT Professional Education open enrollment programs held in Massachusetts. ILP members receive a 15% discount on all MIT Professional Education courses. To obtain the discount code, click here (you must be signed in to this website as an ILP member to access the code).