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Course Criteria
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4.00 Credits
Usability and design as keys to successful technology. Covers user observation techniques, needs assessment, low and high fidelity prototyping, usability testing methods, as well as theory of human perception and performance, and design best practices. Focuses on understanding and applying the lessons of human interaction to the design of usable systems; will also look at lessons to be learned from less usable systems. The course includes several small and one large project.
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4.00 Credits
Introduction to artificial intelligence, focusing on problems of perception, reasoning under uncertainty, and especially machine learning. Supervised learning algorithms. Decision trees. Ensemble learning and boosting. Neural networks, multi-layer perceptrons and applications. Support vector machines and kernel methods. Clustering and unsupervised learning. Probabilistic methods, parametric and non-parametric density estimation, maximum likelihood and maximum a posteriori estimates. Bayesian networks and graphical models: representation, inference and learning. Hidden Markov models. Markov decision processes and reinforcement learning. Computational learning theory.
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4.00 Credits
Introduction to AI, focused on problems in reasoning about action and rational decision making. Search: constraint satisfaction; informed search and optimization; game playing. Knowledge representation and logical inference. Planning: representation, search and heuristics. Bounded rationality, situated agents. Multiagent systems. Discussion of relevant work in philosophy, economics, and decision theory. Applications to scheduling, robotics and e-commerce.
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4.00 Credits
Introduction to computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems. Representing syntactic structure: context-free, augmented context-free, and trans-context-free grammars. Representing semantic structure: first-order and higher-order logics. Computing with syntactic and semantic representations: Prolog programming; parsing and generation algorithms. Low-level language processing with finite-state methods.
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4.00 Credits
Building autonomous robotic systems requires understanding how to make robots that observe, reason, and act. Each component uses many engineering principles: how to fuse, multiple, noisy sensors; how to balance short-term versus long-term goals; how to control one's actions and how to coordinate with others. This year, we will study these questions in the context of a project to develop autonomous robot soccer teams. The class format will mix seminar and lab formats.
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4.00 Credits
Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, encapsulation, data structures, databases, memory management, software development, virtualization, and websites. Languages include C, PHP, and JavaScript plus SQL, CSS, and XHTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for concentrators and non-concentrators alike, with or without prior programming experience.
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4.00 Credits
Abstraction and design in computation. Topics include: Functional and object-oriented styles of programming; software engineering in the small; models of computation. Goal: understanding how to design large programs to make them readable, maintainable, efficient, and elegant. Exercises in ML and Java.
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4.00 Credits
Fundamentals of computer systems programming, machine organization, and performance tuning. This course provides a solid background in systems programming and a deep understanding of low-level machine organization and design. Topics include C and assembly language programming, program optimization, memory hierarchy and caching, virtual memory and dynamic memory management, concurrency, threads, and synchronization.
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4.00 Credits
Supervised individual study of advanced topics in computer science. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. A form available from the Academic Office, Pierce Hall 110, must be filled out and signed by the student and faculty supervisor. Students writing theses may enroll in this course while conducting thesis research and writing.
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4.00 Credits
Laboratory research in topics related to the CPB concentration under the direction of, or approved by, members of the Board of Tutors in CPB. A paper must be submitted to the laboratory sponsor and to the CPB Student Affairs Office for review by the Head Tutor and Course Director.
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