This course covers the development of programs containing a significant amount of …
This course covers the development of programs containing a significant amount of knowledge about their application domain. The course includes a 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).
This class is an applications-oriented course covering the modeling of large-scale systems …
This class is an applications-oriented course covering the modeling of large-scale systems in decision-making domains and the optimization of such systems using state-of-the-art optimization tools. Application domains include: transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning. Modeling tools and techniques include linear, network, discrete and nonlinear optimization, heuristic methods, sensitivity and post-optimality analysis, decomposition methods for large-scale systems, and stochastic optimization. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5223 (System Optimisation: Models and Computation).
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