Statistics 602X
 


Congratulations to the team of Wen Zhou, Cory Lanker, Fangfang Liu, Jia Liu, Ian Mouzon and Wei Zhang for placing 5th on Task 1 in the 2013 Data Mining Cup (DMC)!  See the ISU write-up of their accomplishment here.

Congratulations to the team of Cory Lanker, Guillermo Basulto-Elias, Fan Cao, Xiaoyue Cheng, Marius Dragomiroiu, Jessica Hicks, Ian Mouzon, Lafeng Pan, and Xin Yin for placing 1st in the 2014 Data Mining Cup (DMC)!  See the ISU write-up of their accomplishment here

Here are some resources for Stat 602X:

2013 Syllabus

Some Books

Most Current Version of the Outline/Notes and Slides (the Versions Below Will Remain Frozen as of the End of Semester Sp' 2013)

2013 Course Outline

Slides (A Slightly Condensed Version of the Outline)

Module 1 (Introduction)
Module 2 (Decision Theory, Variance-Bias Tradeoff, Cross-Validation)
Module 3 (Some Linear Theory)

Module 4 (Basic Principal Components)
Module 5 (Non-OLS Linear Predictors Part 1)
Module 6 (Non-OLS Linear Predictors Part 2)
Module 7 (Non-OLS Linear Predictors Part 3)
Module 8 (Linear Prediction Using Basis Functions Part 1)
Module 9 (Linear Prediction Using Basis Functions Part 2)
Module 10 (Linear Prediction Using Basis Functions Part 3)
Module 11 (Smoothing Splines in 1-D)
Module 11A (Penalized Fitting in R^N)

Module 12 (Multi-Dimensional Smoothing Splines)
Module 13 (Kernel Smoothing Methods)
Module 14 (High-Dimensional Use of Low-Dimensional  Smoothers)
Module 15 (Highly Flexible Non-Linear Parametric Regression Methods)
Module 16 (Trees and Related Methods Part 1)
Module 17 (Trees and Related Methods Part 2)

Module 18
(Trees and Related Methods Part 3)
Module 19 (Predictors Built on Bootstrap Samples)
Module 20 (Model Averaging and Stacking)

Module 21 (RKHSs and Penalized/Regularized Fitting Part 1)
Module 22 (RKHSs and Penalized/Regularized Fitting Part 2)
Module 23 (RKHSs and Penalized/Regularized Fitting Part 3)
Module 24 (RKHSs and Penalized/Regularized Fitting Part 4)
Module 25 (Understanding and Predicting Predictor Performance Part 1)

Module 26
(Understanding and Predicting Predictor Performance Part 2)
Module 27 (Generalities About Classification)
Module 28 (Linear Methods of Classification Part 1)
Module 29 (Linear Methods of Classification Part 2)
Module 30 (Support Vector Machines Part 1)
Module 31 (Support Vector Machines Part 2)
Module 32 (Support Vector Machines Part 3)
Module 33 (Support Vector Machines Part 4)
Module 34 (Boosting Part 1)
Module 35 (Boosting Part 2)
Module 36 (Prototype and Nearest Neighbor Methods of Classification)
Module 37 (Association Rules/Market Basket Analysis)
Module 38 (Clustering Part 1)
Module 39 (Clustering Part 2)
Module 40 (Multi-Dimensional Scaling)
Module 41 (Spectral Clustering)
Module 42 (Variations on Principal Components)
Module 43 (Independent Component Analysis)
Module 44 (Density Estimation)
Module 45 (Google PageRanks)
Module 46 (Document Features and String Kernels for Text Processing)
Module 47 (Kernel Mechanics)
Module 48 (Undirected Graphical Models and Machine Learning)

2013 Homework Assignments

Data Sets for Homework

Problem 2: Set1  Set2

Problem 10: Set3

Problem 14: Set4

Problem 16: Set5

Problem 25: Seeds

Problem 27: Spectral

 Exam 1 (Sp '13)
 Key                   
 Exam 2 (Sp '13)
 Key


2011 Course Outline

 Exam 1 (Sp '11)  Key
 Exam 2 (Sp '11)  Key

2011 Homework

2011 Homework 1 Solution
2011 Homework 2 Solution
2011 Homework 3 Solution
2011 Homework 4 Solution
2011 Homework 5 Solution
2011 Homework 6 Solution

 


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Last updated: 05/30/2013