Mathematics (18) - Archived
http://hdl.handle.net/1721.1/33995
Mathematics (18)Tue, 21 Feb 2017 07:32:32 GMT2017-02-21T07:32:32Z18.405J / 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.
Sat, 01 Dec 2001 00:00:00 GMThttp://hdl.handle.net/1721.1/1066712001-12-01T00:00:00Z6.042J / 18.062J Mathematics for Computer Science, Spring 2005
http://hdl.handle.net/1721.1/104427
6.042J / 18.062J Mathematics for Computer Science, Spring 2005
Leiserson, Charles; Lehman, Eric; Devadas, Srinivas; Meyer, Albert R.
This course is offered to undergraduates and is an elementary discrete mathematics course oriented towards applications in computer science and engineering. Topics covered include: formal logic notation, induction, sets and relations, permutations and combinations, counting principles, and discrete probability.
Wed, 01 Jun 2005 00:00:00 GMThttp://hdl.handle.net/1721.1/1044272005-06-01T00:00:00Z6.042J / 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.
Tue, 01 Jun 2010 00:00:00 GMThttp://hdl.handle.net/1721.1/1044262010-06-01T00:00:00Z18.466 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.
Sun, 01 Jun 2003 00:00:00 GMThttp://hdl.handle.net/1721.1/1038142003-06-01T00:00:00Z