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Course Criteria
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3.00 Credits
Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, regulatory analysis. Techniques such as dynamic programming, Markov models, expectation-maximization, local search.
Prerequisite:
graduate standing in biological, computer, mathematical or statistical science, or permission of instructor
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3.00 Credits
Introduction to computational methods for understanding nervous systems and the principles governing their operation. Topics include representation of information by spiking neurons, information processing in neural circuits, and algorithms for adaptation and learning. Offered: jointly with NEUBEH 528.
Prerequisite:
elementary calculus, linear algebra, and statistics, or by permission of instructor
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3.00 Credits
Computational models including deterministic and nondeterministic Turing machines, and techniques for analyzing them. Fundamentals of computability theory and undecidability. Fundamentals of computational complexity theory and NP-completeness. .
Prerequisite:
CSE majors only; CSE 322 or equivalent
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3.00 Credits
Deterministic, nondeterministic, alternating, and probabilistic Turing machines. Time and space complexity, complexity classes, complexity hierarchies, and provably intractable problems.
Prerequisite:
CSE major and CSE 531
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3.00 Credits
Topics in computational complexity more sophisticated than those treated in 532. Topics are expected to vary from year to year, but might typically focus on such areas as parallel complexity, probabilistic complexity, circuit- or automaton-based complexity, or logic.
Prerequisite:
CSE major
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3.00 Credits
Formal approaches to distributed computing problems. Topics vary, but typically include models of distributed computing, agreement problems, impossibility results, mutual exclusion protocols, concurrent reading while writing protocols, knowledge analysis of protocols, and distributed algorithms.
Prerequisite:
CSE major
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3.00 Credits
Principles of simulation of discrete, eventoriented systems. Model construction, simulation and validation. Distributed and parallel simulation techniques. Basic statistical analysis of simulation inputs and outputs. Use of C, an object-oriented language, and S, a statistical analysis package. Prior familiarity with the concepts of probability and statistics desirable.
Prerequisite:
CSE major
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3.00 Credits
Emphasizes the use of analytic models as tools for evaluating the performance of centralized, distributed, and parallel computer systems.
Prerequisite:
CSE major and CSE 451
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3.00 Credits
Data models and query languages (SQL, datalog, OQL). Relational databases, enforcement of integrity constraints. Object-oriented databases and object-relational databases. Principles of data storage and indexing. Queryexecution methods and query optimization algorithms. Static analysis of queries and rewriting of queries using views. Data integration. Data mining. Principles of transaction processing.
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3.00 Credits
Methods for identifying valid, novel, useful, and understandable patterns in data. Induction of predictive models from data: classification, regression, and probability estimation. Discovery of clusters and association rules.
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