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MATH 5392: Introduction to Data Mining
Fall 2017

Table of Contents


Announcement

Make sure that you install H2O and it is working before Oct 23rd lecture. Those who are unable to use H2O may need to install JDK 8. Please follow this link: http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html.

lab2.rmd.txt is uploaded for those who do not have a C compiler installed. Also, it can be knitted into a pdf file. You need to download or save this txt file (do not copy and paste).

Final Presentation

  • 11/30
    • 2:00 - 2:30 DNO
    • 2:35 - 2:55 Zach
  • 12/5
    • 2:00 - 2:20 UTA Mavericks
    • 2:20 - 2:40 UTA 5392-5
    • 2:40 - 3:00 UTA team 2

Check the performance of R

In your R console, type in the following script:

install.packages("SuppDists");
source("https://r.research.att.com/benchmarks/R-benchmark-25.R")

To improve the performance, you can consider using the Microsoft R open. Find more information in https://mran.microsoft.com/documents/rro/installation.

Course Syllabus

Course Schedule

The instructor reserves the right to adjust this schedule in any way that serves the educational needs of the students enrolled in this course.

Week Date Topics Reading Notes
Week1 8/24 Introduction   Lecture 1, Lab 1
Week2 8/29 R Markdown   Lecture 2, Lab 2, Lab 3
Week2 8/31 Review: linear algebra   Lecture 3, HW 1 - Due 9/12
Week3 9/5 Linear regression   Lab 4, Lab 5
Week3 9/7 Linear regression    
Week4 9/12 Linear methods for classification   Lecture 4, HW 2 - Due 9/19
Week4 9/14 Linear methods for classification   Lab 6
Week5 9/19 Model assessment and selection   Lecture 5-I, HW 3 - Due 9/26
Week5 9/21 Elastic net regularization   Lecture 5-II, Lab 7
Week6 9/26 Splines, Generalized Additive models   Lecture 5-III, Lab 8, HW 4 - Due 10/3
Week6 9/28 Splines, Generalized Additive models   Lecture 6
Week7 10/3 Tree-based methods   Lab 9, HW 5 - Due 10/10
Week7 10/5 Tree-based methods   Lecture 7
Week8 10/10 Bagging and random forests   Lab 10, HW 6 - Due 10/17
Week8 10/12 Boosting   Lecture 8
Week9 10/17 Midterm exam   Midterm exam - Due 10/19
Week9 10/19 Final project guidelines, Imputation I   HW 7 - Due 10/26
Week10 10/24 Support vector machines   Lecture 9, Lab 11
Week10 10/26 Support vector machines   HW 8 - Due 11/2
Week11 10/31 Neural networks   Lecture 10
Week11 11/2 Neural networks   Lab 12, HW 9 - Due 11/9
Week12 11/7 Clustering   Lecture 11-I, Lecture 11-II
Week12 11/9 Clustering   Lecture 11-III, HW 10 - Due 11/16
Week13 11/14 Group meeting (no class)   Lecture 12-I, Lecture 12-II
Week13 11/16 Multidimensional scaling   Lab 13, Final HW - Due 12/5
Week14 11/21 Bayesian networks    
Week14 11/23 No class   Thanksgiving holidays
Week15 11/28 Bayesian networks    
Week15 11/30 Final presentations    
Week16 12/5 Final presentations    

Resources

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