Electrical Engineering and Computer Science (6) -
http://hdl.handle.net/1721.1/33986
Electrical Engineering and Computer Science (6)2017-01-17T15:07:44Z6.042J / 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.
2005-06-01T00:00:00Z6.042J / 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.
2010-06-01T00:00:00Z20.430J / 2.795J / 6.561J / 10.539J / HST.544J Fields, Forces, and Flows in Biological Systems (BE.430J), Fall 2004
http://hdl.handle.net/1721.1/103790
20.430J / 2.795J / 6.561J / 10.539J / HST.544J Fields, Forces, and Flows in Biological Systems (BE.430J), Fall 2004
Lauffenburger, Douglas; Grodzinsky, Alan
This course covers the following topics: conduction, diffusion, convection in electrolytes; fields in heterogeneous media; electrical double layers; Maxwell stress tensor and electrical forces in physiological systems; and fluid and solid continua: equations of motion useful for porous, hydrated biological tissues. Case studies considered include membrane transport; electrode interfaces; electrical, mechanical, and chemical transduction in tissues; electrophoretic and electroosmotic flows; diffusion/reaction; and ECG. The course also examines electromechanical and physicochemical interactions in biomaterials and cells; orthopaedic, cardiovascular, and other clinical examples.
2004-12-01T00:00:00Z6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution, Fall 2008
http://hdl.handle.net/1721.1/103560
6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution, Fall 2008
Kellis, Manolis; Galagan, James
This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution
2008-12-01T00:00:00Z