basic mathematical models of computation and the finite representation of infinite objects. The course is slower paced than 6.840J/18.404J. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems.
introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.
http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-876JSpring-2003/CourseHome/index.htm
this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.
http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-871Spring-2005/CourseHome/index.htm
brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; a discussion of technical issues encountered in building a system, including selection of knowledge representation, knowledge acquisition, etc.; and a discussion of current and future research. The course also provides hands-on experience in building an expert system (term project).
laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.
http://ocw.mit.edu/OcwWeb/Mathematics/18-405JFall2001/CourseHome/index.htm
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.
focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders
It discusses the rights and obligations of engineers in connection with educational institutions, government, and large and small businesses. It compares various manners of transplanting inventions into business operations, including development of New England and other U.S. electronics and biotechnology industries and their different types of institutions. The course also considers American systems of incentive to creativity apart from the patent laws in the atomic energy and space fields.
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.