" This course covers concepts and techniques for the design and implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computer-algebra systems, deductive systems, and some artificial intelligence applications. Topics include combinators, generic operations, pattern matching, pattern-directed invocation, rule systems, backtracking, dependencies, indeterminacy, memoization, constraint propagation, and incremental refinement. Substantial weekly programming Assignments and Labs are an integral part of the subject. There will be extensive programming Assignments and Labs, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or some other "functional" language."

Students explore the interface between architecture and engineering. In the associated hands-on activity, students act as both architects and engineers by designing and building a small parking garage.

This course focuses on the complexities associated with security and sustainability of states in international relations. Covering aspects of theory, methods and empirical analysis, the course is in three parts, and each consists of seminar sessions focusing on specific topics.

This course is offered to graduates and is a project-oriented course to teach new methodologies for designing multi-million-gate CMOS VLSI chips using high-level synthesis tools in conjunction with standard commercial EDA tools. The emphasis is on modular and robust designs, reusable modules, correctness by construction, architectural exploration, and meeting the area, timing, and power constraints within standard cell and FPGA frameworks.

Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital theory. Topics discussed also include theories of wage and employment determination, turnover, search, unemployment, equalizing differences, and union behavior. Particular emphasis on the interaction of theoretical and empirical modeling. The aim of this course is to acquaint students with traditional topics in labor economics and to encourage the development of independent research interests. This course is taught in two parts: Fall term and then in the subsequent Fall term.

In this activity, students reinforce their understanding of compound machines by building a catapult. This compound machine consists of a lever and a wheel-and-axel. Catapults have been designed by engineers for a variety of purposes from lifting boulders into the air for warfare to human beings for entertainment; the projectiles in this activity are grapes for a magic act. Given the building materials, students design and build their catapult to launch a grape a certain distance.

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information technology, and graduate (16.413) students.

This course surveys a variety of reasoning, optimization and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.

In this activity, students are challenged to design a contraption using simple machines to move a circus elephant into a rail car. After students consider their audience and constraints, they work in groups to brainstorm ideas and select one concept to communicate to the class.