Decision theory, estimation, confidence intervals, hypothesis testing. Introduces large sample theory. Asymptotic efficiency of estimates. Exponential families. Sequential analysis.
Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.
The "quantum" of economics is the optimizing individual. All of economics ultimately boils down to the behavior of such individuals. Microeconomics studies their basic actions and interactions: individual markets, supply and demand, the impact of taxes, monopoly, etc. Macroeconomics then lumps together these individual markets to study national and international issues. In structure this book—which covers only microeconomics—is not unlike a hiking trip. We start out by putting our boots on and getting our gear together: in Part I we study the optimizing individual. Then we set out on our path and immediately find ourselves hacking through some pretty thick jungle: even simple interactions between just two people (Part II) can be very complicated! As we add even more people (in studying auctions, for example), things get even more complicated, and the jungle gets even thicker. Then a miracle occurs: we add even more people, and a complex situation suddenly becomes simple. After hacking through thick jungle, we find ourselves in a beautiful clearing: competitive markets (Part III) are remarkably easy to analyze and understand.
" This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT."
Systems engineering is an interdisciplinary approach and means to enable the realization of successful systems. It focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, then proceeding with design synthesis and system validation while considering the complete problem including operations, performance, test, manufacturing, cost, and schedule. This course emphasizes the links of systems engineering to fundamentals of decision theory, statistics, and optimization. The course also introduces the most current, commercially successful techniques for systems engineering.
