Online courses directory (10)

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Starts : 2014-09-12
324 votes
Coursera Free Popular Closed [?] Business English Artificial Intelligence Computer Science Economics & Finance

Find out how modern electronic markets work, why stock prices change in the ways they do, and how computation can help our understanding of them.  Build algorithms and visualizations to inform investing practice.

Starts : 2003-02-01
11 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

Starts : 2009-09-01
9 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

Starts : 2004-02-01
15 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.

Starts : 2004-02-01
7 votes
MIT OpenCourseWare (OCW) Free Closed [?] Business Graduate MIT OpenCourseWare Sloan School of Management

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.

Starts : 2009-09-01
11 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

Starts : 2013-02-01
9 votes
MIT OpenCourseWare (OCW) Free Business MIT OpenCourseWare Sloan School of Management Undergraduate

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.

Starts : 2003-09-01
9 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.

Starts : 2006-02-01
13 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

This seminar is intended for doctoral students and discusses topics in applied probability. This semester includes a variety of fields, namely statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), computer science (random K-SAT problem, coloring, average case complexity) and electrical engineering (low density parity check (LDPC) codes).

Starts : 2003-06-01
11 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.

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