Prerequisites: Grade of C or better in CS 310 and STAT 344.
Instructor: Prof. Harry Wechsler email@example.com
Course Description – Concepts and techniques in data mining and multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges.
Goals: Critical Thinking (look for Pitfalls); Model Selection and Predictive Analytics Using Cross-Validation and Training; Meaningful (size and scope) Data Mining Application (to find useful patterns); Experimental Design, Metrics and Performance Evaluation; Theory vs. Practice.
Time, Day, and Venue: MWF, 3:45 pm – 6:45 pm
– Nguyen Engineering Building 1107
Office Hours: MWF 2:45 – 3:30 pm or by appointment, ENGR 4448.
First day of classes: June 29, 2015
No class on Friday, July 3, 2015
Last day of classes: Wednesday, July 29, 2015
Final Exam: Friday, July 31, 2015
Required Textbook: P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006. http://www-users.cs.umn.edu/~kumar/dmbook/index.php
Complementary Textbook 1: J. Han and M. Kamber, Data Mining (3rd ed.) Morgan Kaufmann, 2011. http://web.engr.illinois.edu/~hanj/bk3/bk3_slidesindex.htm
Complementary Textbook 2: I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.), Morgan Kaufmann, 2011. http://www.cs.waikato.ac.nz/ml/weka/book.html
Complementary Textbook 3: T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (2nd ed.), Springer, 2009. http://statweb.stanford.edu/~tibs/ElemStatLearn/
Complementary Textbook 4: A. Rajaraman, J. Leskovec, and J. D. Ullman, Mining of Massive Datasets (2nd ed.), Cambridge University Press, 2014. http://infolab.stanford.edu/~ullman/mmds/book.pdf
Software and Data:
UCI Machine Learning Repository is a repository of databases and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. http://archive.ics.uci.edu/ml/
Kaggle is the home of data science and data mining competitions. http://www.kaggle.com/
Resources: Software and Data. http://www-users.cs.umn.edu/~kumar/dmbook/resources.htm
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. http://mallet.cs.umass.edu/
R – Programming language for statistical computing and graphics. http://en.wikipedia.org/wiki/R_%28programming_language%29 and http://www.r-project.org/
MATLAB and Toolboxes – The Language of Technical . http://www.mathworks.com/products/matlab/
CLOSED BOOK EXAMINATIONS
· Homework – 20% // late homework not accepted //
· Midterm – Monday, July 13, 2015 – 20 %
· Team Term Project and FINAL Review – July 27, 2015
and July 29, 2015 – 20 %
· (Cumulative) Final – July 31, 2015 - 40 %
You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies