SSC 385 Modern Statistic Methods

Instructor: Dr. Maggie Myers

Office: PAI 5.48 Phone: 4719533

Email: myers@cs.utexas.edu

Office hours:
Topic
This course is intended to illustrate current approaches in modeling and computation in statistics.
Intended audience
Masters and Ph.D. students in machine learning, data mining, computational biology, engineering, psychology, and other fields in need of advanced modeling tools in their research.
Methods of evaluation
Homework, participation, midterm, and projects. Assignments will be made throughout the semester and will generally be due a week after assigned. A final project involves giving a brief presentation of some subtopic of interest.
Text (optional)
DeGroot, Morris H. and Mark J. Schervish, Probability and Statistics, 3rd ed. Addison Wesley, 2002.
The text is a reference and will be supplemented. Information will be organized on the classÍs electronic blackboard (http://courses.utexas.edu)
Syllabus

Background

Probability and Bayes Rule

Random Variables and Vectors: Discrete and Continuous

Using Bayesian Networks

Expectation, Variance, and the Central Limit Theorem

Inference

Estimation and Distributions of Estimators
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Bayesian Modeling, Prior and Posterior Distributions

MLE/MAP/EM Parameter Estimation

Testing Hypotheses

Models

Linear Statistical Models

Mixture Models

Markov Models / Random Walks

Hidden Markov Models

Simulations

Bootstrapping

Markov Chain Monte Carlo Methods

Randomized Sampling Algorithms (Monte Carlo)

Metropolis Hastings Algorithms

Gibbs Sampling
These concepts will be illustrated using applications drawn from machine learning, computational biology, and other interests of the students enrolled. Students will apply techniques to data sets using R and other appropriate packages.