This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (It) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading.
Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.
Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with extended discussion of interpretation of results and simulation and data analysis for illustration.
Subject:
Mathematics and Statistics, Science and Technology, Social Sciences
This course will provide students with the fundamentals of computational problem-solving techniques that are used to understand and predict properties of nanoscale systems. Emphasis will be placed on how to use simulations effectively, intelligently, and cohesively to predict properties that occur at the nanoscale for real systems. The course is designed to present a broad overview of computational nanoscience and is therefore suitable for both experimental and theoretical researchers. While some aspects of the simulation methods such as numerical algorithms will be presented, there will be little if any programming required. Rather, we will emphasize the intelligent application (as opposed to “black box” use) of codes and methods, and the connection between the computer results and the physical properties of the problem.
" This is a course in how corporations make use of the insights and tools of risk management. Most courses on derivatives, futures and options, and financial engineering are taught from the viewpoint of investment bankers and traders in the securities. This course is taught from the point of view of the manufacturing corporation, the utility, the software firm—any potential end-user of derivatives, but not the dealer. Most related courses focus on the extensive taxonomy of instruments and the complex models developed to price them, and on ways to exploit mispricing. While this course will make use of some of these pricing models, the focus is on how corporations use the insights and models to improve their operations, to increase the value of their real assets, or to create the financial flexibility necessary to implement their core strategy."
This course is an introduction to the analytical tools that support design and decision-making in real estate and infrastructure development. There is a particular focus on identifying and valuing sources of flexibility using “real options”, Monte-Carlo simulation, and other techniques from the field of engineering systems. This course integrates economic and engineering perspectives, and is suitable for students with various backgrounds. It serves to provide useful preparation for thesis work in the area. The course applies the approach to the design and phasing of a mega infrastructure real estate project. Note This MIT OpenCourseWare site is based, in part, on materials on Design for Real Estate and Infrastructure Development from Professor de Neufville's and Professor Geltner's Web site.
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