Computer Science
Computational Science: COMPSCI 369 Semester 1, City Campus
J. Tinsley Oden, Associate Vice President for Research, The University of Texas at Austin
Computational Science (called also Scientific Computing, or Numerical Analysis) is the design, development, application, and analysis of computer algorithms and software to solve scientific and engineering problems. It includes not only numerical methods, probabilistic modelling, computer-based statistical inference, and computer simulation required for solving underlying systems of math equations, but also computer visualization, statistical analysis, and interpretation of computed solutions.
This course will be useful to students interested in algorithms for modelling and solving applied problems (in particular, in bioinformatics or engineering), and will teach problem-solving and modelling skills that will be of broad application. All necessary biology or engineering background will be taught during the course. This course provides an overview of algorithms and computing techniques using examples either from computational biology and bioinformatics or engineering and vision-guided control. It provides a hands-on introduction to important computational topics including applied linear algebra, dynamic programming and string algorithms, Markov models, heuristic search algorithms, tree algorithms and modeling techniques. Problems in biology are focused on genome comparisons (sequence alignment) and phylogenetic reconstruction. Problems in engineering are focused on signal / image processing (modeling, filtering, matching, and 3D reconstruction from stereo images).
The examinable material in this course is summarised in the expected learning outcomes.
These are based on lectures, graded assignments, and your reading of the recommended books. All of these ingredients are necessary if you are to get the most out of the course. There are two assignments (worth 15% each) designed to complement the material from class, as well as the reading from recommended textbooks and Internet resources.
Required Text
There is no required textbook because no single textbook covers all of the
material covered in the course. Reading materials drawn from several
textbooks, as well as the primary literature will be useful.
Recommended Reading
- Computational science and engineering:
- Heath M. T.: Scientific Computing: An Introductory Survey , (McGraw-Hill 2002).
- Kleinberg J. and Tardos E. : Algorithm Design, (Addison Wesley 2006).
- Schaefer M. : Computational Engineering - Introduction to Numerical Methods, (Springer-Verlag 2006; e-Resource at voyager.auckland.ac.nz)
- Strand G. : Computational Science and Engineering, (Wellesley - Cambridge Press 2007)
- Computational biology:
- Durbin R, Eddy S.R., Krogh A. and Mitchison G. : Biological
Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids,
(Cambridge University Press 1998).
- Higgs P.G. and Attwood T.K. : Bioinformatics and Molecular Evolution, (Blackwell Publishing, 2005)
- Jones N.C. and Pevzner P.A. :
An Introduction to Bioinformatics Algorithms,
(MIT Press, 2004).
- Durbin R, Eddy S.R., Krogh A. and Mitchison G. : Biological
Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids,
(Cambridge University Press 1998).
Solving practical problems with computational engineering techniques
(figures 1.2 and 8.1 from the book of M. Schaefer).
For assistance with course material and course work you should visit the marker during his office hours. Assistance can also be obtained from course lecturers during their published office hours or from the course supervisor. The Department of Computer Science also has a team of support staff (see the posters around the labs for support contacts) who are happy to provide guidance on more general issues to do with your study in computer science.
If you miss a lecture, you should catch up as soon as possible by reading the corresponding sections of the textbook. If you miss the deadline for an assignment and have a valid reason, you should see the course supervisor. If you miss the test/exam for any valid reason, or you sit the test/exam but believe that your performance was impaired for some reason, then you may be able to apply for an aegrotat, compassionate or special pass consideration.
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