Undergraduate Courses
CSE 373: Data Structures And Algorithms
Fundamental algorithms and data structures for implementation. Techniques for solving problems by programming. Linked lists, stacks, queues, directed graphs. Trees: representations, traversals. Searching (hashing, binary search trees, multiway trees). Garbage collection, memory management. Internal and external sorting. Intended for non-majors. Cannot be taken for credit if credit received for CSE 332. Prerequisite: CSE 123 or CSE 143.
CSE 417: Algorithms And Computational Complexity
Design and analysis of algorithms and data structures. Efficient algorithms for manipulating graphs and strings. Fast Fourier Transform. Models of computation, including Turing machines. Time and space complexity. NP-complete problems and undecidable problems. Intended for non-majors. Prerequisite: CSE 373.
Professional (Evening) Courses
CSEP 521: Applied Algorithms
Principles of design of efficient algorithms with emphasis on algorithms with real world applications. Examples drawn from computational geometry, biology, scientific computation, image processing, combinatorial optimization, cryptography, and operations research. Prerequisite: CSE majors only.
CSEP 531: Computability And Complexity Theory
Survey of the theory of computation including Turing Machines, Churche' s Thesis, computability, incompleteness, undecidability, complexity classes, problem reductions, Cook' s theorem, NP-completeness, randomized computation, cryptography, parallel computation, and space complexity. Some emphasis will be placed on historical and philosophical aspects of the theory of computation. Prerequisite: CSE PMP majors only.
Graduate Courses
CSE 515: Statistical Methods In Computer Science
Introduction to the probabilistic and statistical techniques used in modern computer systems. Graphical models, probabilistic inference, statistical learning, sequential models, decision theory. Prerequisite: either STAT 341 or STAT 391, and graduate standing in computer science, or permission of instructor.
CSE 521: Design And Analysis Of Algorithms I
Principles of design of efficient algorithms: recursion, divide and conquer, balancing, dynamic programming, greedy method, network flow, linear programming. Correctness and analysis of algorithms. NP-completeness. Prerequisite: CSE major and CSE 326 or equivalent. CSE majors only.
CSE 522: Design And Analysis Of Algorithms Ii
Analysis of algorithms more sophisticated than those treated in 521. Content varies and may include such topics as algebraic algorithms, combinational algorithms, techniques for proving lower bounds on complexity, and algorithms for special computing devices such as networks or formulas. Prerequisite: CSE major and CSE 521.
CSE 524: Parallel Algorithms
Design and analysis of parallel algorithms: fundamental parallel algorithms for sorting, arithmetic, matrix and graph problems and additional selected topics. Emphasis on general techniques and approaches used for developing fast and efficient parallel algorithms and on limitations to their efficacy. Prerequisite: CSE major and CSE 521.
CSE 525: Randomized Algorithms And Probablisitc Analysis
Examines algorithmic techniques: random selection, random sampling, backwards analysis, algebraic methods, Monte Carlo methods, and randomized rounding; random graphs; the probabilistic method; Markov chains and random walks; and analysis tools: random variables, moments and deviations, Chernoff bounds, martingales, and balls in bins. Prerequisite: CSE 521 or equivalent; CSE majors only. Offered: WSp.
CSE 527: Computational Biology
Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as network reconstruction and analysis, sequence analysis, regulatory analysis and genetic analysis. Techniques such as Bayesian networks, Gaussian graphical models, structure learning, expectation-maximization. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.
CSE 528: Computational Neuroscience
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. Prerequisite: elementary calculus, linear algebra, and statistics, or by permission of instructor. Offered: jointly with NEUBEH 528.
CSE 529: Neural Control Of Movement: A Computational Perspe
Systematic overview of sensorimotor function on multiple levels of analysis, with emphasis on the phenomenology amenable to computational modeling. Topics include musculoskeletal mechanics, neural networks, optimal control and Bayesian inference, learning and adaptation, internal models, and neural coding and decoding. Prerequisite: vector calculus, linear algebra, MATLAB, or permission of instructor. Offered: jointly with AMATH 533; W.
CSE 531: Computational Complexity I
Deterministic and nondeterministic time and space complexity, complexity classes, and complete problems. Time and space hierarchies. Alternation and the polynomial-time hierarchy. Circuit complexity. Probabilistic computation. Exponential complexity lower bounds. Interactive proofs. Prerequisite: CSE majors only; CSE 322 or equivalent.
CSE 532: Computational Complexity Ii
Advanced computational complexity including several of the following: circuit complexity lower bounds, #p and counting classes, probabilistically-checkable proofs, de-randomization, logical characteristics of complexity, communication complexity, time-space tradeoffs, complexity of data structures. Prerequisite: CSE majors only; Recommended: CSE 531.
CSE 536: Theory Of Distributed Computing
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.