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Course Criteria
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3.00 Credits
Introduction to theory and computation of macromolecular structure. Principles of biopolymer structure: computer representations and database search; molecular dynamics and Monte Carlo simulation; statistical mechanics of protein folding; RNA and protein structure prediction (secondary structure, threading, homology modeling); computer-aided drug design; proteomics; statistical tools (neural networks, HMMs, SVMs). Prerequisites: basic knowledge algorithmic design (Computational Biology and Bioinfomatics 230 or equivalent), probability and statistics (Statistics 213 and 244 or equivalent), molecular biology (Biology 118 or equivalent), and computer programming. Alternatively, consent of instructor. Instructor: Schmidler
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3.00 Credits
Introduction to the study of temporal patterns in nonequilibrium systems. Theoretical, computational, and experimental insights used to explain phase space, bifurcations, stability theory, universality, attractors, fractals, chaos, and time-series analysis. Each student carries out an individual research project on a topic in nonlinear dynamics and gives a formal presentation of the results. Prerequisites: Computer Science 6, Mathematics 107, and Physics 41L, 42L, or equilavent. Instructor: Behringer or Virgin
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3.00 Credits
Design and analysis of algorithms and representations for artificial intelligence problems. Formal analysis of techniques used for search, planning, decision theory, logic, Bayesian networks, robotics, and machine learning. Prerequisite: Computer Science 100 and Computer Science 130. Instructor: Conitzer or Parr
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3.00 Credits
Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 270. Prerequisite: Computer Science 100, Mathematics 104, and Statistics 103 or consent of instructor. Instructor: Parr
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3.00 Credits
Image formation and analysis; feature computation and tracking; image motion analysis; stereo vision; image, object, and activity recognition and retrieval. Prerequisites: Mathematics 104 or 107; Mathematics 135 or Statistics 104; Computer Science 6. Instructor: Tomasi
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3.00 Credits
Instructor: Staff
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1.00 Credits
Introduction for graduate students in computer science. Topics for discussion include: computer science as a research discipline, views of what constitutes a research contribution, approaches to research in different subfields, tools and methodologies, publishing and presenting research results, the role of computer science as an "amplifier" in other sciences, ethical and policy issues, the information technology industry, grants and funding, and guidelines for success as a graduate student and as a scientist. Instructor: Staff
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3.00 Credits
Not open to students who have taken Computer Science 332. Instructor: Staff
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3.00 Credits
A selection of advanced topics from the areas of digital computer architectures and fault-tolerant computer design. Prerequisite: Electrical Engineering 252 or equivalent. Instructor: Staff
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3.00 Credits
Theory of advanced VLSI design. Specifications development, methodology, issues, circuit-level trade-offs. Full custom design, standard cell design, gate array design, silicon compilation. Semiconductor technologies and logic families for semi-custom design. Clocking schemes and distribution, race conditions. Design of a variety of circuits (adders, I/O drivers, RAM, FIFO, etc.) Testing of all phases in the life cycle of an integrated circuit. Top-down design and bottom-up implementation. Student projects. Not open to students who have taken Computer Science 310 before Fall 1994. Prerequisite: Electrical Engineering 261 or equivalent. Instructor: Kedem
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