Online courses directory (19947)
The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.
This course covers theory and evidence on government expenditure policy-- topics include: The theory of public goods; Education; State and local public goods; Political economy; Redistribution and welfare policy; Social insurance programs such as social security and unemployment insurance; and Health care policy.
In the trilogy of D-Lab courses, D-Lab: Dissemination focuses on disseminating innovations among underserved communities, especially in developing countries. Students acquire skills related to building partnerships and piloting, financing, implementing, and scaling-up a selected innovation for the common good. The course is structured around MIT and outside competitions. Teams develop an idea, project or (social) business plan that is "ready to roll" by term's end. Course includes an on-line forum discussion board, student-led case studies and a final proposal or business plan for realizing your dream innovation.
This course will study the question of Global Architecture from the point of view of producing a set of lectures on that subject. The course will be run in the form of a writing seminar, except that students will be asked to prepare for the final class an hour-long lecture for an undergraduate survey course. During the semester, students will study the debates about where to locate "the global" and do some comparative analysis of various textbooks. The topic of the final lecture will be worked on during the semester. For that lecture, students will be asked to identify the themes of the survey course, and hand in the bibliography and reading list for their lecture.
This subject is concerned with quantitative methods for analyzing large-scale water resource problems. Topics covered include the design and management of facilities for river basin development, flood control, water supply, groundwater remediation, and other activities related to water resources. Simulation models and optimization methods are often used to support analyses of water resource problems. In this subject we will be constructing simulation models with the MATLAB® programming language and solving numerical optimization problems with the GAMS optimization package.
This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment.
Additional Faculty
Div Bolar
Dr. Bradford Dickerson
Dr. John Gabrieli
Dr. Doug Greve
Dr. Karl Helmer
Dr. Dara Manoach
Dr. Jason Mitchell
Dr. Christopher Moore
Dr. Vitaly Napadow
Dr. Jon Polimeni
Dr. Sonia Pujol
Dr. Bruce Rosen
Dr. Mert Sabuncu
Dr. David Salat
Dr. Robert Savoy
Dr. David Somers
Dr. A. Gregory Sorensen
Dr. Christina Triantafyllou
Dr. Wim Vanduffel
Dr. Mark Vangel
Dr. Lawrence Wald
Dr. Susan Whitfield-Gabrieli
Dr. Anastasia Yendiki
Other Versions
Other OCW Versions
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