Duke University 2009-2010 - Office of the Registrar - Duke University

Development of network application programs based on the client-server model. Remote procedure call and
implementation of remote procedure call. Prerequisite: knowledge of the C programming language. Instructor: Staff.
3 units.
215. Wireless Networking and Mobile Computing. 3 units. C-L: see Electrical and Computer Engineering 256
216. Data-Intensive Computing Systems. Data-Intensive Computing Systems. Principles and techniques for making
intelligent use of the massive amounts of data being generated in commerce, industry, science, and society. Topics
include indexing, query processing, and optimization in large databases, data mining and warehousing, new abstractions
and algorithms for parallel and distributed data processing, fault-tolerant and self-tuning data management for cloud
computing, and information retrieval and extraction for the Web. Prerequisites: Computer Science 116 or an introductory
database course or consent of instructor. Instructor: Babu or Yang. 3 units. C-L: Computational Biology and
Bioinformatics 233, Computational Biology and Bioinformatics 265
219. Statistical Data Mining. 3 units. C-L: see Statistics and Decision Sciences 218
220. Advanced Computer Architecture I. Fundamental aspects of advanced computer architecture design and analysis.
Topics include processor design, pipelining, superscalar, out-of-order execution, caches (memory hierarchies), virtual
memory, storage systems, simulation techniques, technology trends and future challenges. Prerequisite: Computer
Science 104 or Electrical and Computer Engineering 152 or equivalent. Instructors: Board, Kedem, Lebeck, or Sorin.
3 units. C-L: Electrical and Computer Engineering 252
221. Advanced Computer Architecture II. Parallel computer architecture design and evaluation. Design topics include
parallel programming, message passing, shared memory, cache coherence, cache coherence, memory consistency
models, symmetric multiprocessors, distributed shared memory, interconnection networks, and synchronization.
Evaluation topics include modeling, simulation, and benchmarking. Prerequisite: Computer Science 220 or Electrical
and Computer Engineering 252 or consent of instructor. Instructor: Lebeck or Sorin. 3 units. C-L: Electrical and
Computer Engineering 259
225. Fault-Tolerant and Testable Computer Systems. 3 units. C-L: see Electrical and Computer Engineering 254
226. Probability for Electrical and Computer Engineers. 3 units. C-L: see Electrical and Computer Engineering 255
230. Design and Analysis of Algorithms. Design and analysis of efficient algorithms. Algorithmic paradigms.
Applications include sorting, searching, dynamic structures, graph algorithms, randomized algorithms. Computationally
hard problems. NP completeness. Prerequisite: Computer Science 100 or equivalent. Instructor: Agarwal, Arge, or
Reif. 3 units.
232. Approximation Algorithms. Cover traditional approximation algorithms with combinatorial and linear
programming techniques; extended survey of cut problems and metric embeddings; embeddings, dimensionality
reduction, locality sensitive hashing, and game theory. Instructor: Munagala. 3 units.
234. Computational Geometry. Models of computation and lower-bound techniques; storing and manipulating
orthogonal objects; orthogonal and simplex range searching, convex hulls, planar point location, proximity problems,
arrangements, linear programming and parametric search technique, probabilistic and incremental algorithms.
Prerequisite: Computer Science 230 or equivalent. Instructor: Agarwal, Edelsbrunner, or Reif. 3 units. C-L:
Computational Biology and Bioinformatics 234, Computational Biology and Bioinformatics 264
235. Topics in Data Compression. Emphasis on the redundancies found in textual, still-frame images, video, and voice
data, and how they can be effectively removed to achieve compression. The compression effects in information
processing. Additional topics may include information theory, the vulnerability of compressed data to transmission
errors, and the loss of information with respect to the human visual system (for image data). Available compression
technologies and the existing compression standards. Prerequisites: Computer Science 130 and 208 or Computer
Science 254 or Electrical Engineering 282. Instructor: Markas or staff. 3 units.
236. Computational Topology. Introduction to topology via graphs; facts about curves and surfaces; representing
triangulations; discussion of simplicial complexes; emphasis on Delaunay and alpha complexes and on homology
groups; computational via matrix reduction; Morse functions; PL functions; Reeb graphs; development of persistent
homology; proof of stability; applications and extensions. Prerequisite: Computer Science 230. Instructor: Edelsbrunner
or Harer. 3 units. C-L: Mathematics 264
237. Randomized Algorithms. Models of computation, Las Vegas and Monte Carlo algorithms, linearity of expectation,
Markov and Chebyshev inequalities and their applications, Chernoff bound and its applications, probabilistic methods,
expanders, Markov chains and random walk, electric networks and random walks, rapidly mixing Markov chains,
randomized data structures, randomized algorithms for graph problems, randomized geometric algorithms, number
theoretic algorithms, RSA cryptosystem, derandomization. Prerequisite: Computer Science 230. Instructors: Agarwal
and staff. 3 units.
240. Computational Complexity. Turing machines, undecidability, recursive function theory, complexity measures,
reduction and completeness, NP, NP-Completeness, co-NP, beyond NP, relativized complexity, circuit complexity,
alternation, polynomial time hierarchy, parallel and randomized computation, algebraic methods in complexity theory,
communication complexity. Prerequisite: Computer Science 140 or equivalent. Instructor: Agarwal. 3 units.
Departments, Programs, and Course Offerings 83