Mathematics (18) - Archived
http://hdl.handle.net/1721.1/33995
Mathematics (18)2017-04-24T14:41:07Z5.95J / 6.982J / 7.59J / 8.395J / 18.094J / 1.95J / 2.978J Teaching College-Level Science and Engineering, Fall 2012
http://hdl.handle.net/1721.1/107185
5.95J / 6.982J / 7.59J / 8.395J / 18.094J / 1.95J / 2.978J Teaching College-Level Science and Engineering, Fall 2012
Rankin, Janet
This participatory seminar focuses on the knowledge and skills necessary for teaching science and engineering in higher education. This course is designed for graduate students interested in an academic career, and anyone else interested in teaching. Topics include theories of adult learning; course development; promoting active learning, problem-solving, and critical thinking in students; communicating with a diverse student body; using educational technology to further learning; lecturing; creating effective tests and assignments; and assessment and evaluation. Students research and present a relevant topic of particular interest. The subject is appropriate for both novices and those with teaching experience.
2012-12-01T00:00:00Z18.405J / 6.841J Advanced Complexity Theory, Fall 2001
http://hdl.handle.net/1721.1/106671
18.405J / 6.841J Advanced Complexity Theory, Fall 2001
Spielman, Daniel
The topics for this course cover various aspects of complexity theory, such as  the basic time and space classes, the polynomial-time hierarchy and the randomized classes . This is a pure theory class, so no applications were involved.
2001-12-01T00:00:00Z6.042J / 18.062J Mathematics for Computer Science, Spring 2010
http://hdl.handle.net/1721.1/104426
6.042J / 18.062J Mathematics for Computer Science, Spring 2010
Meyer, Albert R.
This subject offers an introduction to Discrete Mathematics oriented toward Computer Science and Engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in Computer Science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.
2010-06-01T00:00:00Z18.466 Mathematical Statistics, Spring 2003
http://hdl.handle.net/1721.1/103814
18.466 Mathematical Statistics, Spring 2003
Dudley, Richard
This graduate level mathematics course covers decision theory, estimation, confidence intervals, and hypothesis testing. The course also introduces students to large sample theory. Other topics covered include asymptotic efficiency of estimates, exponential families, and sequential analysis.
2003-06-01T00:00:00Z