Lecture times: Mondays 6 - 9 PM
Lecture location: Forsyth Building 236

About the Course

This course introduces concepts, algorithms, and techniques of data mining on different types of datasets, including (1) matrix data, (2) text data, (3) set data, (4) sequence data, (5) time series, (6) graph and network, and (7) image data. The class project involves hands-on practice of mining useful knowledge from large data sets. The course is a graduate-level computer science course, which is also a good option for senior-level computer science undergraduate students interested in the field. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining systems to analyze large amounts of data.

Class Schedule

Prerequisites

• CS 5800 or CS 7800, or consent of instructor
• The students are expected to have knowledge in data structures, algorithms, basic linear algebra, and basic statistics. You will also need to be familiar with at least one programming language, and have programming experiences.

Grading

• Homework: 40%
• Midterm exam: 25%
• Course project: 30%
• Participation: 5%

*Note: all the deadlines are 11:59PM (midnight) of the due dates; No late submissions accepted!

Regrading Policy:

• If you have doubts in your grading, please submit a regrading form (via emails to both TAs and CC to the Instructor) indicating clearly the reason why you think it should be regraded
• The deadline of the regrading form should be submitted within one week after you receive your score
• We will regrade the whole homework/exam

Textbook

Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011

Recommended books for further reading:

1. "Data Mining: The Textbook" by Charu Aggarwal (http://www.charuaggarwal.net/Data-Mining.htm)
2. "Data Mining" by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (http://www-users.cs.umn.edu/~kumar/dmbook/index.php)
3. "Machine Learning" by Tom Mitchell (http://www.cs.cmu.edu/~tom/mlbook.html)
4. "Introduction to Machine Learning" by Ethem ALPAYDIN (http://www.cmpe.boun.edu.tr/~ethem/i2ml/)
5. "Pattern Classification" by Richard O. Duda, Peter E. Hart, David G. Stork (http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html)
6. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
7. "Pattern Recognition and Machine Learning" by Christopher M. Bishop (http://research.microsoft.com/en-us/um/people/cmbishop/prml/))

Q & A

You are encouraged to come to the office hours of TAs and the instructor.

Peer-based Q&A via Piazza: piazza.com/northeastern/fall2015/cs622001/home

Academic Integrity Policy

A commitment to the principles of academic integrity is essential to the mission of Northeastern University. The promotion of independent and original scholarship ensures that students derive the most from their educational experience and their pursuit of knowledge. Academic dishonesty violates the most fundamental values of an intellectual community and undermines the achievements of the entire University.

For more information, please refer to the Academic Integrity Web page.