INF383D:
**Mathematical Foundations of Information Studies** (Spring 2010)

**Instructor**: Matt Lease

**Day and Time**: Wednesdays 1:30-4:30pm

**Location**: Meeting in
UTA 1.212. This hands-on course will split time between the classroom and the UTA 1.210C computing laboratory.

This course will provide hands-on learning of basic statistics and probability using the interactive R programming language. Enrollment will be limited to 11 to allow close interaction between students and the instructor and foster a supportive environment for those with limited computing background. Besides grounding theory in a concrete, applied setting, knowledge of "R" will provide a practical skill students can subsequently apply for analyzing quantitative information arising within their particular area of study or interest.

**Handouts**

- 1/20/10: Lab 1
- 1/27/10: Lab 2
- 2/03/10: Lab 3
- Assignment 3
- Notes on marginal and conditional probabilities (2/8/10)
- Probability chapter: see 1.2, 1.3, & 1.5 (see also: slides)

- 2/10/10: Lab 4
- 2/24/10: Lab 5
- 3/03/10: Lab 6
- 3/10/10: Lab 7
- Notes on Bernoulli and Binomial expectation and variance

- 3/24/10: Lecture Notes
- 3/31/10:
- 4/14/10:
- 4/21/10: Lab 10
- 4/27/10: Lab 11
- 5/05/10: Lab 12

**Course Project**: you are free to choose, but here are some ideas

- examine the relation between words used in product reviews and product scores: data
- Datamob data
- UCI Machine Learning data

**Course Textbook**: John Verzani, Using R for Introductory Statistics, 2004

- Our library has an ebook version which has different layout but the SAME content (publisher has confirmed). Page numbers may differ.
- Supporting website
- Errata (only 6 pages, be sure to refer to this for each chapter)
- Answers to selected problems

**Resources**

- Download R for your personal computer: OS X and Windows.
- R website
- R wiki
- Handy R reference card
- R Language Definition
- Verzani's earlier simpleR -- Using R for Introductory Statistics (searchable PDF)
- Manual "An Introduction to R": HTML PDF
- R for Matlab users: guide

**Highlights**

- Statistics: mean, variance, standard deviation, quartiles and quantiles, correlation (Pearson, Spearman), central limit theorem, confidence intervals, analysis of variance, regression models, sampling, significance testing, plots and charts
- Probability: discrete and continuous distributions, marginals, conditionals, parametric families, expectation
- Programing: variables, fuctions, control flow, scope, ...