MIDDLE EAST TECHNICAL UNIVERSITY
DEPT. OF COMPUTER ENGINEERING
CEng 574 STATISTICAL DATA ANALYSIS
Instructor Volkan Atalay
e-mail vatalay AT metu.edu.tr
Class Thursday 9:40-12:30 (on-line)
Office Hour TBA
Course web page address http://blog.metu.edu.tr/vatalay/ceng-574-statistical-data-analysis/
The objective of this course is to introduce the concepts and techniques of clustering and multivariate and exploratory data analysis. This course also offers an opportunity to perform data analysis by using data visualization, projection and embedding.
Prerequisites Knowledge of programming, probability, and linear algebra.
Main Reference Book
Alpaydın, Introduction to Machine Learning, 2nd Edition (2010) or 3rd Edition (2014), The MIT Press.
(Yapay Öğrenme, Turkish language edition, translated by the author, Boğaziçi Üniversitesi Yayınevi, 1st Edition 2011, 2nd Edition 2013, 3rd Edition 2017)
1 Data representation, distance metrics and similarity measures
2 Linear and non-linear projection methods, data embedding methods
3 Data clustering algorithms and methods
4 Evaluation of clustering algorithms and validation of clusterings
5 Applications of data clustering in various fields such as bioinformatics and data stream analysis
Assignment #1, #2, #3 2pts each 6
Assignment #4, #5, #6 8pts each 24
Assignment #7, #8, #9, #10 4pts each 16
Paper submission and presentation 15
Attendance and class participation 10
Notes and Remarks
Students coming from graduate programs other than the Computer Engineering program should attend the first class.
Attend the first lecture at https://cengvideo.ceng.metu.edu.tr/b/m-v-k63-d43
We will use ODTUClass for the conduct and for all of the materials for this course.
Assignments should be done on individual basis.
Dataset Analysis will be performed in a team setting of 2 persons.
R programming language will be used for the applied part of this course.
Late submission policy: you have 4 days of late submission.
Academic Integrity Guide for Students: http://oidb.metu.edu.tr/system/files/Academic%20Integrity%20Guide%20for%20Students.pdf
Interactive Demonstrations of Some of the Algorithms from the Course
by Mehmet Akif Akkus, Begüm Yağmur, Shakiba R, Abdullah Al-shiabi
Step by step k-means algorithm on a 2D interactive environment: http://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/
Step by step Mean-shift algorithm on a 2D interactive environment: http://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/mean-shift/
An Introduction to R, http://cran.r-project.org/doc/manuals/r-release/R-intro.html
HSAUR3: A Handbook of Statistical Analyses Using R (3rd Edition), Torsten Hothorn and Brian S. Everitt, Chapman & Hall/CRC, 2014, https://cran.r-project.org/web/packages/HSAUR3/
R tutorials, December 10, 2015, By Tal Galili, https://www.r-bloggers.com/how-to-learn-r-2/
R Tutorial: Introduction to R, https://www.youtube.com/watch?v=7cGwYMhPDUY
Introduction to Data Science with R – Data Analysis Part 1, https://www.youtube.com/watch?v=32o0DnuRjfg
Also, https://www.r-project.org/ look at “Documentation”, “Manuals” and https://cran.r-project.org/ see “Contributed”
A Tutorial on Principal Component Analysis, Jonathon Shlens, 2014, http://arxiv.org/pdf/1404.1100v1.pdf
A tutorial on Principal Components Analysis, Lindsay I Smith, February 26, 2002, http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
Principal Component Analysis in R, Gregory B. Anderson, 2013, http://www.ime.usp.br/~pavan/pdf/MAE0330-PCA-R-2013
Principal Components Analysis: A How-To Manual for R, Emily Mankin, http://people.tamu.edu/~alawing/materials/ESSM689/pca.pdf
5 functions to do Principal Components Analysis in R, Gaston Sanchez,
R-Bloggers., Computing and visualizing PCA in R, [online] 2013, http://www.r-bloggers.com/computing-and-visualizing-pca-in-r/
Step by step implementation of PCA in R using Lindsay Smith’s tutorial, http://stats.stackexchange.com/questions/90331/step-by-step-implementation-of-pca-in-r-using-lindsay-smiths-tutorial
PCA in R, Ed Boone, https://www.youtube.com/watch?v=Heh7Nv4qimU
Principal Components Analysis Using R – P1, Steve Pittard, https://www.youtube.com/watch?v=5zk93CpKYhg