Courses
This is for IE7 to hold div open
BIO 318M: Biostatistics
Course Summary: BIO 318M is an introductory/intermediate statistics course suitable for both a general audience and biological science students. Principles of data organization, presentation, summation, analysis and interpretation will be covered using iological and social examples. The course is designed to assist students with analyses and interpretation of data. The course emphasizes understanding of the material above memorization, which is accomplished by focusing on active learning , course work and group activities.
Course Objectives: The course is designed to promote student's critical thinking and problem solving skills, to enable students to master statistical tests frequently used within the biological arena and to decide when these tests are appropriate. Following the course, students will be able to design experiments, determine and conduct the appropriate analysis of the most common data types. During the course, students will learn to review biological and general literature, with a mind tocritical evaluation of the experimental design and results. Students will acquire an appreciation for communities (social, samples and populations), positive and negative errors (the imperfection of all experiments), risk assessment, decision making and how all of these can affect their endeavors in both the biological and personal arenas.
Instructor: C. Wilke |
Instructor: M. Meyers |
Instructor: J. Bryant |
Unique#: 51055 |
Unique#: 51075 |
Unique#: 51095 |
Unique#: 51060 |
Unique#: 51080 |
Unique#: 51100 |
Unique#: 51065 |
Unique#: 51085 |
Unique#: 51105 |
Unique#: 51070 |
Unique#: 51090 |
Unique#: 51110 |
BIO 384K: "Muddyboots Statistics"
Unique#: 53060
W 9:00 - 12:00 PM
PAT 617
Instructor: D. Bolnick
This is a graduate course aimed at graduate students in the fields of ecology, evolution, behavior, and physical antrhopology, that aims to address commonly used statistical tools in these disciplines. Basic analyses that are often covered in introuctory biostatistical courses (ANOVA, regression, categorical data analysis) will be given brief treatment, and most of the time will be spent on advanced analyses that participating students identify as being of particular use. Emphasis will be placed on when and how to use particular analyses, using the statistical programming language R as a platform for analyses. This class is intended to provide a hands-on practical approach to statistics.
CS 395T: Computational Statistics with Application to Bioinformatics
Unique#: 55899
MW 1:30 - 3:00 PM
PAR 201
Instructor: William H. Press
Bayesian vs. frequentist approaches. Common univariate and multivariate distributions, statistical tests, contingency tables. Random deviates, Monte Carlo and stochastic simulation, bootstrap methods. Model fitting, multidimensional optimization, simulated annealing. Gaussian mixture models, EM methods. Hidden Markov models, Markov chain Monte Carlo and its geneeralizations. Entropy,mutual information., applications to clustering and classification. Emphasis willbe on paracitcal methods of computation with examples drawn mostly from bioinformatics, especially genomics, but with utility for other fields dealing with large amounts of experimental or observational data. Indicidual or collaborative projects using actual data (provided either by the student or by the instructor) will be encouraged.
Prerequisites: Graduate standing or upper-division undergraduate with consent of instructor, mathematics at least including undergraduate multivariable calculus and linear algebra; must be comfortable programming in C++, Java, or C.
ME 367S: Simulation Modeling
#18230
TTH 12:30 - 2:00 PM
ETC 2.144
This is an elective Mechanical Engineering undergraduate junior/senior level course. The students learn basic systems modeling, random number generation, the ARENA simulation language, input and output data analysis and design of simulation experiments. It is particularly useful for advanced manufacturing and semiconductor industries' applications.
ORI 390R-2: Mathematical Statistics
#18770
TTH 3:30 - 5:00 PM
ETC 5.132
This is a required course for the graduate ORIE students.
List of Topics: Multiple random variables, properties of a random sample, principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotic evaluations.
CAM 395T: Data Mining: Statistical Learning Perspectives
#65780
MW 9:30 - 11:30 AM
PAR 303
Restrictions: Graduate standing required.
CS 329E : Elements of Computing: Algorithms for Bioinformatics
#55575
Instructor: Tandy Warnow
TTH 12:30 - 2:00 PM
ECJ 1.204
*Are you interested in biology, but don't know much about it?
*Would you like to know how biologists use software to answer deep biological questions?
*Would you like to learn how to design algorithms, but don't know how to program them?
*Do you like to program, and would you have fun designing programs that can analyze DNA sequences and discover interesting things?
If you answered yes to any of these questions, this course may be just for you! This course will introduce you to the modern world of bioinformatics, covering not only basic biology (in a very easy way, don't worry!), but also how to design algorithms, what it means for a problem to be "NP-hard" and how to deal with that, and how biologists put all these things togeter to make powerful software that can shed light on fundamental problems. If you know how to program (for example, if you've already taken the Elements courses that are prerequisites to this course), you will be able to apply that skill to some interesting problems. If you don't know how to program, you can still take the course! I am organizing this so that everyone who is interested in this area will have a blast! Feel free to email me at tandy@cs.utexas.edu if you have any questions.
CS 395T : Algorithms for Computational Biology
#55890
Instructor: Tandy Warnow
TTH 2:00 - 3:30 PM
ECJ 1.204
The course topic is algorithm design in computational molecular biology, but we will focus our study on two related topics: multiple sequence alignment and phylogeny (evolutionary history) reconstruction. These problems are intimately related because the inference of an evolutionary history generally involves first obtaining a multiple alignment of the sequences, and then reconstructing a tree on the aligned sequences (though a simultaneous approach can also be taken). Both of these problems are individually enormously computationally intensive - computational approaches generally involve attempts to solve NP-hard optimization problems, and months of analysis can be used to estimate the phylogeny, without any guarantee of optimality. Thus, algorithmic development, firmly grounded in mathematical theory, is needed by the biological research community.
The goal of the course is to enable the students to do high quality research (both mathematically and algorithmic) for both mulitple sequence alignment and
phylogeny reconstruction. The mathematical foundations of phylogeny reconstruction are quite elegant and deep, so most of our discussion will be focused there.
We will cover the statistical models used to describe evolution of molecular sequences, the primary optimization problems, and the mathematical theory needed to predict the performance of reconstruction methods under the models. The course will also present a large spectrum of algorithms (both deterministic with established theory, and heuristics) that have been developed for these problems. In addition, we will have several lectures from guest biologists with whom I collaborate.
No background in biology is assumed for this course. The mathematics used in developing algorithms for both phylogeny reconstruction and mulitple sequence alignment combines combinatorics and graph theory (including the theory of chordal graphs), complexity theory, and probability and statistics. Students with good preparation in at least one of these areas will be able to begin doing algorthm development early on. However, abundant research opportunities exist for those students without significant mathematical training: simulaton studies of existing methods is an important methodology within the field, and has resulted in many highly influential and important publications in major scientific journals. Therefore, students of all backgrounds (including from biology) are welcome.
CS 395T : Computational Statistics with Application to Bioinformatics
#55899
Instructor: Dr. Wiliam H. Press
MW 1:30 - 3:00 PM
PAR 201
Bayesian vs. frequentist approaches. Common univariate and multivariate distributions, statistical tests, contingency tables. Random deviates, Monte Carlo and stochastic simulation, bootstrap methods. Model fitting, multidimensional optimization, simulated annealing. Gaussian mixture models, EM methods. Hidden Markov models, Markov chain Montel Carlo and its generalizations. Entropy, mutual information, applications to clustering and classification.
Emphasis will be on practical methods of computation with examples drawn mostly from bioinformatics, especially genomics, but with a utility for other fields dealing with large amounts of experimental or observational data. Individual or collaborative projects using actual data (provided either by the student or by the instructor) will be encouraged.
Prerequisites: Graduate standing, or upper-division undergraduate with consent of instructor; mathematics at least including undergraduate multivariable calculus and linear algebra; must be comfortable programming in C++, Java, or C.
CS 395T : Probability and Statistics for Computer Science
#55916
Instructor: Dr. Maggie Myers
TTH 9:30 - 11 AM
RLM 6.188
CAM 384L: Theory of Probability
#65690
TTH 11:00 - 12:30 PM
Restrictions: Consent of instructor must be obtained. Graduate standing required. Prerequisite: CAM 384K or M385C.
CAM 384S: Mathematical Statistics
#65695
TTH 5:00 - 6:30 PM
Restrictions: Graduate standing required. Prerequisite: CAM 384R or M384C.
CAM 384U: Analsys of Variance
#65700
MWF 10:00 - 11:00 AM
RLM 6.118
Restrictions: Prerequisite: M378K or the equivalent or consent of instructor.
CAM 382L: Numerical Methods in Petroleum Engineering
#65675
MWF 11:00 - 12:00 PM
CPE 2.210
Restrictions: Graduate standing required.
CAM 383: Advanced Numerical Methods
#65680
MWF 2:00 - 3:00 PM
WRW 413
Restrictions: Graduate standing in Computational and Applied Mathematics, Engineering, or Geology. Students seeking to enroll in any seminar must present technical prerequisites satisfactory to the instructor.
CAM 383D: Numerical Analysis: Int/App/Quad/Diff Eq
#65685
TTH 12:30 - 2:00 PM
TAY 3.144
Restrictions: Graduate standing required. Prerequisite: either consent of instructor or M427K and 365C; and CAM 383C, CS 383C, or M383E.
CAM 393D: Numerical Differental Equations II
#65750
MWF 11:00 - 12:00 PM
RLM 9.166
Restrictions: None
CAM 393N: Numerical Methods for Flow and Transport Problems
#65755
MW 4:30 - 6:00 PM
WRW 413
Restrictions: Graduate standing required.
CAM 394F: Finite Element Methods
#65770
MWF 2:00 - 3:00 PM
WRW 312
Restrictions: Graduate standing required.
CAM 394H: Advanced Theory of Finite Element Methods
#65775
MWF 10:00 - 11:00 AM
WRW 413
Restrictions: Graduate standing required. Prerequisite: CAM 394F or EM 394F, and EM 386L or the equivalent.
CAM 395T: Parallel Computation for Scientists and Engineers
#65785
TTH 12:30 - 2:00 PM
CPE 2.206
Restrictions: Graduate standing required.
CAM 397: Computational Electromagnetics
#65794
TTH 2:00 - 3:30 PM
ENS 126
Restrictions: None
CAM 397: Stabil/Multiscale Methods in CFD
#65805
TTH 2:00 - 3:30 PM
WEL 3.402
Restrictions: Graduate standing required.
EDP 371: Introduction to Statistics
This course is designed to help students learn the introductory descriptive and inferential statistical procedures that are used in behavioral and social science research studies. Students will learn the assumptions underlying the hypotheses being tested by, and the inferences that can be made with the use of the procedures. These skills will provide the student with a basis to conduct their own such analyses and to evaluate critically others' uses of statistics.
#09995
TTH 3:30 - 5:00 PM
Instructor: Beretvas
#09985
TTH 12:30 - 2PM
Instructor: Pituch
#09980
TTH 9:30 - 11:00 AM
Instructor: Vaughn
EDP 382K: Correlation & Regression
Simple and multiple linear regression, residual analysis, transformations, model building with real data, testing models, mediation and moderation using multiple regression. Prerequisite: One graduate course in Statistics.
#10120
WED 9:00 AM - 12:00 PM
Instructor: Keith
EDP 382K: Survey of Multivariate Methods
A practical introduction to the analysis of multivariate data as applied to examples from the social sciences. Multivariate linear model, principal components and factor analysis, discriminant analysis, clustering and canonical correlation. Prerequisiste: Graduate courses in Experimental Design and Correlation and Regression or the equivalent.
#10125
TTH 4:00 - 5:30 PM
Instructor: Koch
EDP 382K: Applied Bayesian Analysis
A practical introduction to Bayesian statistical interference, with an emphasis on applications in behavioral and measurement research. Examination of how Bayesian statistical inference differs from classical inference in the context of simple statistical procedures and models, such as hypothesis testing, ANOVA and regression. Prerequisite: Graduate courses in Experimental Design and Correlation and Regression or the equivalent.
#10115
MW 10:30 AM - 12:00 PM
Instructor: Vaughn
EDP 382K: Structural Equation Modeling
Introduction to the basic concepts, methods and computing toools of structual equation modeling. Emphasis will be placed on developing a working familiarity with some of the common statistical procedures, coupled with their application through the use of statistical software. Prerequisite: Graduate courses in Experimental Design and Correlation and Regression or the equivalent.
#10130
WED 1:00 - 4:00 PM
Instructor: Whittaker
EDP 384: Meta-Analysis
An introduction to statistics used to synthesize statistical results from a set of studies. Course content can include calculation of different effect sizes, calculating pooled estimates using fixed and random effects models, testing moderating variables using fixed and mixed effects models, test of heterogeneity of effect sizes, assessing and correcting publication bias. Prerequisites: Graduate course in Experimental Design and Correlation and Regression or the equivalent.
#10190
TTH 9:30 - 11:00 AM
Instructor: Beretvas
EDP 384: Data Analysis using SAS
Introduces students to the use of SAS for database manipulations, including creating data flies, reading data files, use of arrays, loops, and macros. Prerequisite: Experimental Design or the equivalent.
#10185
THU 2:00 - 5:00 PM
Instructor: Whittaker
EDP 482K: Experimental Design and Statistical Inference
Principles, construction and analysis of experimental designs. Includes one-way classification, randomized blocks, Latin squares, factorial and nested designs. Fixed and random effects, multiple comparisons, and analysis of covariance. Prerequisite: One graduate course in Statistics.
#10135 (Non EDP Majors)
FRI 9:00 AM - 12:00 PM
Instructor: Borich
#10140
(EDP Majors)
TTH 9:30 - 11 AM
TH 11:00 AM - 12:00 PM
Instructor: Pituch
M316: Elementary Statistical Methods
Prerequisite: A score of at least 430 on the SAT Mathematics Level 1 Test, or M 301 with grade at least C.
58605 MWF 9:00-10:00A WAG 101
58610 TTH 9:30-11:00A GEO 2.216
58615 MWF 12:00-1:00P PHR 2.110
58620 MWF 3:00-4:00P GEO 2.216
M 349R: Applied Regression and Time Series
Prerequisite: A course in statistics, plus consent of the Actuarial advisor.
58950 TTH 3:30-5:00P BUR 216 CEPPARO
M 358K: Applied Statistics
Prerequisite: M 362K with grade at least C
58960 MWF 10:00-11:00A RLM 6.104 DANIELS
58965 TTH 11:00-12:30P RLM 6.104 RHODES
M 362K: Probability 1
Prerequisite: M 408D or 408L with grade at least C.
59010 MWF 9:00-10:00A RLM 5.118 DURBIN
59015 TTH 11:00-12:30P RLM 5.114 RADIN C.
59020 MWF 12:00-1:00P RLM 7.120
59025 TTH 12:30-2:00P ART 1.120
59030 MWF 1:00-2:00P RLM 6.124
59035 MWF 2:00-3:00P RLM 6.118
M 362M: Introduction to Stochastic Processes
Prerequisite: M 362K with grade at least C
59040 TTH 11:00-12:30P RLM 7.120 ZITKOVIC
M 378K: Introduction to Mathematical Statistics
Prerequisite: M 362K with grade at least C
59160 MWF 10:00-11:00A RLM 6.120 BATCHELOR
59165 MWF 11:00-12:00P RLM 5.118 BATCHELOR
M 384D: Mathematical Statistic II (same as CAM 384S )
Prerequisite: M 384C
59200 TTH 5:00-6:30P RLM 12.166 PARKER
M 384E: Analysis of Variance (same as CAM 384U )
Prerequisite: M 378K or equivalent
59205 MWF 10:00-11:00A RLM 6.118 SMITH