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

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 Sequence Biology. Introduction to algorithmic and computational issues in 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. Prerequisites: basic knowledge algorithmic design (Computer
Science 230 or equivalent), probability and statistics (Statistics 213 or equivalent), molecular biology (Biology 118 or
equivalent). Alternatively, consent 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 Xray
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
263B. Computational Structural Biology. 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 (STA 213 and 244 or equivalent), molecular biology (Biology 118 or equivalent),
and computer programming. Alternatively, consent of instructor. Instructor: Schmidler. 3 units. C-L: Computational
Biology and Bioinformatics 250, Statistics and Decision Sciences 277
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, 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. 3 units.
274. Introduction to Computer Vision. 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. 3 units.
296. Advanced Topics in Computer Science. Instructor: Staff. 3 units.
297. Advanced Topics in Computer Science. Advanced topics from various areas of computer science, changing each
year. Includes research intensive work exposing the student to computer science research methodology and resulting
in a major document or project. Instructor: Staff. 3 units.
For Graduate Students Only
300. Introduction for Graduate Students in Computer Science. 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. 1 unit.
Departments, Programs, and Course Offerings 84