Duke University 2008-2009 - Office of the Registrar - Duke University

andomized 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.
250. Numerical Analysis. Error analysis, interpolation and spline approximation,
numerical differentiation and integration, solutions of linear systems, nonlinear equations,
and ordinary differential equations. Prerequisites: knowledge of an algorithmic
programming language, intermediate calculus including some differential equations, and
Mathematics 104. Instructor: Rose or Sun. 3 units. C-L: Mathematics 221, Statistics and
Decision Sciences 250
261. Computational Biology of Gene Regulation. Provides a systematic introduction to
algorithmic and computational issues present in the analysis of biological sequences: DNA,
RNA, and protein. Emphasizes probabilistic approaches and machine learning methods, e.g.
Hidden Markov models. Explores applications in genome sequence assembly, protein and
DNA homology detection, gene and promoter finding, motif identification, models, of
regulatory regions, comparative genomics and phylogenetics, RNA structure prediction,
post-transcriptional regulation. Lecture and discussions of primary literature. Prerequisites:
basic knowledge of algorithm design (CPS 230 or equiv), probability and statistics (STA 213
or equiv), molecular biology (BIO 118 or equiv), and computer programming. Alternatively,
consent of instructor. Instructor: Ohler or Hartemink. 3 units. C-L: Computational Biology
and Bioinformatics 261
262. Computational Systems Biology. Provides a systematic introduction to algorithmic
and computational issues present in the analysis of biological systems. Emphasizes
probabilistic approaches and machine learning methods. Explores modeling basic biological
processes (e.g., transcription, splicing, localization and transport, translation, replication,
cell cycle, protein complexes, evolution) from a systems biology perspective. Lectures and
discussions of primary literature. Prerequisites: basic knowledge of algorithm design
(Computer Science 230 or equivalent), probability and statistics (Statistics 213 or
equivalent), molecular biology (Biology 118 or equivalent), and computer programming.
Alternatively, consent of instructor. Instructor: Hartemink or Ohler. 3 units. C-L:
Computational Biology and Bioinformatics 262
263. Algorithms in Structural Biology and Biophysics. Introduction to algorithmic and
computational issues in structural molecular biology and molecular biophysics. Emphasizes
geometric algorithms, provable approximation algorithms, computational biophysics,
molecular interactions, computational structural biology, proteomics, rational drug design,
and protein design. Explores computational methods for discovering new pharmaceuticals,
NMR and X-ray data, and protein-ligand docking. Prerequisites: basic knowledge of
algorithm design (Computer Science 230 or equivalent), probability and statistics (Statistics
213 or equivalent), molecular biology (Biology 118 or equivalent), and computer
programming. Alternatively, consent of instructor. Instructor: Donald. 3 units. C-L:
Computational Biology and Bioinformatics 263, Structural Biology and Biophysics 263
264. Nonlinear Dynamics. 3 units. C-L: see Physics 213
270. Artificial Intelligence. 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: Parr. 3 units.
271. Machine Learning. Theoretical and practical issues in modern machine learning
techniques. Topics include statistical foundations, supervised and unsupervised learning,
Courses of Instruction 109