This course includes materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics, which will introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence. The material is introductory; the readings cite many resources outside those assigned in this course, and students are encouraged to explore these resources to pursue topics of interest. Upon successful completion of this course, the student will be able to: Describe the major applications, topics, and research areas of artificial intelligence (AI), including search, machine learning, knowledge representation and inference, natural language processing, vision, and robotics; Apply basic techniques of AI in computational solutions to problems; Discuss the role of AI research areas in growing the understanding of human intelligence; Identify the boundaries of the capabilities of current AI systems. (Computer Science 405)
Surveys research which incorporates psychological evidence into economics. Prospect theory. Biases in probabilistic judgment. Self-control and mental accounting with implications for consumption and savings. Fairness, altruism, and public goods contributions. Financial market anomalies and theories. Impact of markets, learning, and incentives. Some evidence on memory, attention, categorization, and the thinking process.
Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers.This course is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 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, which serve as the basis for discussion and assignments for the course.
Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers. Meets with graduate subject 6.833 but assignments differ. 6.803/6.833 is a course in the department's "Artificial Intelligence and Applications" concentration.
Quantitative techniques of operations research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, and pickup and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, and emergency repair services). Unified study of functions of random variables, geometrical probability, multi-server queuing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. Computer exercises and discussions of implementation difficulties.
Quantitative techniques of operations research with emphasis on applications in transportation systems analysis (urban, air, ocean, highway, and pickup and delivery systems) and in the planning and design of logistically oriented urban service systems (e.g., fire and police departments, emergency medical services, and emergency repair services). Unified study of functions of random variables, geometrical probability, multi-server queuing theory, spatial location theory, network analysis and graph theory, and relevant methods of simulation. Computer exercises and discussions of implementation difficulties.
Intensive coverage of precision engineering theory, heuristics, and applications pertaining to the design of systems ranging from consumer products to machine tools. Topics covered include: economics, project management, and design philosophy; principles of accuracy, repeatability, and resolution; error budgeting; sensors; sensor mounting; systems design; bearings; actuators and transmissions; system integration driven by functional requirements and operating physics. Emphasis on developing creative designs which are optimized by analytical techniques applied via spreadsheets. Many real-world examples are given, and classwork and tests are based on mini-design problems. From the course home page: This is a projects course with lectures consisting of design teams presenting their work and the class helping to develop solutions; thereby everyone learning from everyone's projects.
Introduction to mathematical modeling, optimization, and simulation, as applied to manufacturing. Specific methods include linear programming, network flow problems, integer and nonlinear programming, discrete-event simulation, heuristics and computer applications for manufacturing processes and systems. Restricted to Leaders for Manufacturing students. One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.
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