stat 430

Empirical Methods for Computer Scientists

Fall 2011

Tuesday & Thursday. 2:10–3:30. Town 0206

Heike Hofmann, hofmann@iastate.edu.
Office hours: TBD

Susan Vanderplas, srvanderplas+stat430@gmail.com.
Office hour: Fridays 12-1 in 1414 Snedecor

Project - Scoreboard

Rank Team MSE - ArrDelay
0 Joint Forces 123.3767
1 EECSER 129.7390
2 BCB2 131.6779
3 CKNS 132.3142
4 BCB1 134.1214
5 Awesome 135.0597
6 Flight 93 135.3212
Rank Team MSE - Delayed
0 Joint Forces 0.04361618
1 BCB2 0.04478434
2 Awesome 0.04516484
3 Flight 93 0.04557436
4 CKNS 0.04604437
5 EECSER 0.04620251
6 BCB1 0.04908957

Syllabus

Course syllabus describing objectives, modules and assessment.

Lectures and timetable

Date Lecture Notes Homework and Resources
Probability & Distributions
Aug 23 Sample Spaces, Kolmogorov Axioms, Counting HW #1, due Aug 30
Aug 25 Conditional Probability, Independence, Bayes Rule, see also Ewens & Grant, p. 3-6, 36-41 or Baron p.
Aug 30 Bayes, Discrete Random Variables, Expectation, see Ewens & Grant, p. 6,7, 13-16; Baron p. HW #2, due Sep 6
Sep 1 Moment generating function, discrete distributions, see Ewens & Grant, p. 7-19
Sep 6 Joint discrete distributions, intro to continuous distributions, see Ewens & Grant, p. 55-57, 24, 28, 42 HW #3, due Sep 13
Sep 8 Continuous random variables and special distributions
Sep 13 Central Limit Theorem and Corollaries, Relationship: Poisson and Gamma, Multivariate random variables, convolution see Ewens & Grant, p. 59-60, 75-76, 133. HW #4, due Sep 20
Basic Simulations
Sep 15 Intro to Random Number Generators, R code
Sep 20 Review of material using Simulations., R code Review material for exam. previous exam. focus on questions 3 and 4.
Statistical Inference
Sep 22 Midterm Exam (2:10 - 3:00).
Intro to Statistics: graphical summaries and estimation
Sep 27 Review of exam, Graphical summaries and estimation.
Sep 29 Parameter Estimation, Intro to Confidence Intervals Project I, due dates Nov 3/Nov 8 (midnight).
Oct 4 Confidence Intervals: large and small samples HW #5, due Oct 11
Oct 6 Intro to R, R code, R reference card
Statistical Models
Oct 11 Hypothesis testing, R code, data HW #6, due Oct 18
Oct 13 Intro to Linear Models: Regression and Anova
Oct 18 Normal Model, Residuals and Prediction in Linear Models, Model Comparisons, R code
Oct 20 General Linear Models in R, R code
Oct 25 Midterm Exam II, Logistic Regression HW #7, due Nov 1, cheese data , mystery
Oct 27 Logistic Regression R code
Nov 1 Trees and Random Forests in R, R code no homework this week - focus on project submission
Specialized Topics of interest
Advanced Simulations
Nov 3 More Theoretical look on Trees and Random Forests, , R code
Nov 8 Bootstrapping, Intro to Markov Chains HW #8, due Nov 15, Final Project, due dates Nov 15, Nov 29, Dec 6
Nov 10 Markov Chains
Nov 15 Hidden Markov Models, R code
Nov 17 Metropolis Hastings, Gibbs Sampling, R code
Nov 22/24 Thanksgiving Break
Nov 29 Clustering Methods, R code
Dec 1 Clustering, Project Discussion
Final Projects and Exam
Presentations Tuesday, December 6, 2:10 - 2:30
Date Presenter Title
2:10 - 2:35 Jennifer Chang, Liu Jie, Kejue Jia, Ruolin Liu Diabeties
2:35 - 3:00 Sudhanshu Vyas, Chetan Govindaiah, Kyoung Cho, Nicholas Booher Identification of Fault Critical Areas in FPGAs
3:00 - 3:25 Jinsheng Zhang, Jingwei Yang, Mei Li, Zhang Zhang, Haihua Xie Evaluation of Quality of Wines
Presentations Thursday, Dec 8, 2:10 - 3:30
Date Presenter Title
2:10 - 2:35 Katherine Wilkins, Benjamin Mulaosmanovic, Kannan Sankar Prediction of Breast Tumor Malignancy in Inconclusive Samples Using Logistic Regression
2:35 - 3:00 Marisol Martinez-Escobar, Chad Nelson, Kuan Wu Tumor segmentation
3:00 - 3:25 Nate Bowerman, Jenny Woody, Michael Svendsen, Sylvia Do Predicting Upcoming Wins in the NHL
Dec 15
7:30 - 9:30 am (finals week) Final Exam

Useful links