DMTM 2015 - 01 Course Introduction
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The document is an introduction to the Data Mining and Text Mining course (UIC 583) taught by Prof. Pier Luca Lanzi at Politecnico di Milano. It outlines the course structure, including basic and advanced methods, as well as resources and evaluation methods. Key materials include textbooks and software tools such as R, Python, and RapidMiner.
DMTM 2015 - 01 Course Introduction
- 1. Prof. Pier Luca Lanzi Course Introduction Data Mining andText Mining (UIC 583 @ Politecnico di Milano)
- 2. Prof. Pier Luca Lanzi Data Mining and Text Mining • Prof. Pier Luca Lanzi Dipartimento di Elettronica, Informazione e Bioingegneria pierluca.lanzi@polimi.it voice: 02 23993472 http://www.deib.polimi.it/people/lanzi • Office Hours Wednesday, from 14:30 until 16:00 2
- 3. Prof. Pier Luca Lanzi Course Structure • Introduction to basic Data Mining and Text Mining methods (24 hours) • Advaced Techniques and Applications (16 hours) • Final Project will involve an application to real-world data 3
- 4. Prof. Pier Luca Lanzi Course Outline • What is Data Mining? • Data and knowledge representation • Data exploration and preparation • Data Mining tasks § Associations § Clustering § Classification • Advanced techniques and applications § Text Mining § Graph Mining § Data Streams 4
- 5. Prof. Pier Luca Lanzi syllabus
- 6. Prof. Pier Luca Lanzi
- 7. Prof. Pier Luca Lanzi Course Material • “Data Mining and Analysis: Fundamental Concepts and Algorithms,” Mohammed Zaki and Wagner Meira Jr. Cambridge University Press in 2014. http://www.dataminingbook.info • “Mining of Massive Datasets Book,” by A. Rajaraman, J. Ullman. http://www.mmds.org • Course slides available on BEEP and articles distributed during the course • Software § R Rstudio (http://www.rstudio.com) § Python/IPython (numpy, scipy, scikit, etc.) § BigML (http://www.bigml.com) § Rapid Miner/Weka (http://rapid-i.com/) 7
- 8. Prof. Pier Luca Lanzi Additional Material • “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Second Edition, February 2009, Trevor Hastie, Robert Tibshirani, Jerome Friedman (http://statweb.stanford.edu/~tibs/ElemStatLearn/) • “An Introduction to Data Science,” Jeffrey Stanton https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf • Ian H. Witten, Eibe Frank. “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations” 2nd Edition. 8
- 9. Prof. Pier Luca Lanzi R Help Websites • Quick-R http://www.statmethods.net • R Cookbook http://www.cookbook-r.com • R Bloggers http://www.r-bloggers.com • R on Stackoverflow http://stackoverflow.com/tags/r/info • Google R Styleguide https://google-styleguide.googlecode.com/svn/trunk/Rguide.xml 9
- 10. Prof. Pier Luca Lanzihttp://www.kdnuggets.com/2012/08/poll-analytics-data-mining-programming-languages.html
- 11. Prof. Pier Luca Lanzi http://xkcd.com/353/
- 12. Prof. Pier Luca Lanzi Evaluation • May 2015 First Midterm (15 points) • June 2015 Second Midterm (18 points) • July 2015 Full exam for those who failed midterms 12
- 13. Prof. Pier Luca Lanzi Challenges and exercises might be proposed during the course to substitute part of the written exam There is also another way to pass the exam http://www.kaggle.com http://www.drivendata.org/ http://tunedit.org/data-competitions