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 (BMB-4)
Office Hour Tuesday 14:40-15:40 (office and online)
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 10pts each 30
Assignment #7 6pts 6
Paper submission and presentation 18
Attendance and class participation 10
Notes and Remarks
Students coming from graduate programs other than the Computer Engineering program should attend the first class.
The students should prove to have been fully vaccinated or regularly tested against Covid-19 to participate in class.
The students should wear a mask at all times during the class.
The students should provide their own HES codes to the instructor and to the teaching assistant.
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 a total of 4 days of late submission.
Academic Integrity Guide for Students: http://oidb.metu.edu.tr/system/files/Academic%20Integrity%20Guide%20for%20Students.pdf
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