CS 395T Computational Statistics with Application to Bioinformatics
CAM 383M Statistical and Discrete Methods for Scientific Computing
Department: The University of Texas at Austin, Department of Computer Sciences
Instructor: Professor William H. Press
Now being given: Spring, 2011
Previously offered: Spring, 2008; Spring, 2009; Spring, 2010.
Meets for lectures: MW 1:30 - 3:00 PM in PAI 3.14, and occasionally F 1:30 - 3:00 in WEL 4.224
Meets for discussion section and computational workshop: F 1:30 - 3:00 in WEL 4.224
Current Information for Spring 2011:
For up-to-date information see the
CS395T/CAM383M Discussion Forum
The rest of this page has information about the course in general.
is a practical course in applying (mostly) modern statistical
techniques to (mostly) real data, particularly bioinformatic data and
large data sets. There is only a small amount of theorem proving; the
emphasis is on efficient computation and concise coding, mostly in
MATLAB (where we learn various data-parallel language idioms) and C++
(which we learn to interface seamlessly to MATLAB for convenience
and computational power).
covered include probability theory and Bayesian inference; univariate
distributions; Central Limit Theorem; generation of random deviates;
tail (p-value) tests; multiple hypothesis correction; empirical
distributions; model fitting; error estimation; contingency tables;
multivariate normal distributions; phylogenetic clustering; Gaussian
mixture models; EM methods; maximum likelihood estimation; Markov
Chain Monte Carlo; principal component analysis; dynamic programming;
hidden Markov models; performance measures for classifiers; support
vector machines; Wiener filtering; wavelets; multidimensional interpolation;
and concept list:
A detailed course outline, with links to complete lecture
notes (PDF slide files) is here,
from Spring, 2010
. The list of concepts
associated with each lecture is provided as a study guide.
Earlier versions from previous years are also available: 2009
(Instructors at other institutions may obtain PowerPoint versions of
these files on request.)
standing, or upper-division undergraduate with consent of instructor.
Mathematics at least including undergraduate multivariable calculus
and linear algebra is assumed, as well as some programming experience
in MATLAB, C++, and/or Java (or, possibly, Mathematica, Fortran, or
C). A previous course in undergraduate level probability and
statistics is helpful, but not required.
no required text.
However, many lectures will utilize methods
in Numerical Recipes, Third Edition
. Enrolled students will be provided
with a free electronic subscription to this book, as well as access
to its source code.
Some other relevant books are:
Enrolled students are expected to attend lectures.
Occasional (not regular) problem sets or computer exercises will
be assigned. Students are expected to contribute to the course wiki. A
student project or paper is required (may be collaborative). No
written exams, but there will be individual final oral interviews
(20-30 min.) covering the lecture material.
2010 course forum:
The 2010 course discussion forum
has additional materials, links to references, and discussion contributed
by students. (Also includes archived 2009 course forum.)
2008 course wiki:
The 2008 course wiki
earlier version of the lecture notes, with some discussion threads and
contributions by students. (Unfortunately, some of these are hard
to find, since they are attached to individual slide links in the lectures.)