CS426: Mining Massive Datasets
Jun 13: Solution for Homework 3 posted.
Jun 06: Project 2 posted. Due on July 02, 23:59pm.
May 31: Homework 3 posted. Due on June 09.
May 30: Solution for Homework 2 posted.
May 20: Solution for Homework 1 posted.
May 17: Homework 2 posted. Due on May 27.
May 02: Homework 1 posted. Due on May 15.
May 02: Project 1 posted. Due on May 08, 23:59pm.
Apr 25: Deadline for group formation is Apr 28.
Apr 21: Course website launched.
This course will introduce basic and advanced techniques for massive datasets processing. Topics include: data mining basics, cloud computing platforms, programming models and MapReduce, large scale machine learning and data mining algorithms, and data-intensive applications. The goal of this course is to help students understand and exploit the techniques of a new computing paradigm called data-intensive scalable computing (DISC).
Wu-Jun Li (firstname.lastname@example.org; http://www.cs.sjtu.edu.cn/~liwujun; Rm 3-537, SEIEE Building; 34206661)
Office Hours: TBD
Zhi-Qin Yu (email@example.com; Rm 3-503, SEIEE Building)
Office Hours: TBD
Mon 14:00 - 15:40
Wed 10:00 - 11:40
Fri 08:00 - 09:40
Rm 105, Dong Shang Yuan (东上院 105)
[MMDS]: Anand Rajaraman and Jeffrey D. Ullman. Mining of Massive Datasets. Cambridge University Press, 2011.
( You can download it from the book website.)
[DM]: Jiawei Han, and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, Second Edition, 2006.
The English reprint edition (英文影印版) can be bought through China-Pub.
[PRML]: Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
[HA]: Chuck Lam. Hadoop in Action. Manning Publications, First Edition, 2010.
[Aliyun]: 周憬宇，李武军，过敏意.《飞天开放平台编程指南-阿里云计算的实践》. 电子工业出版社，2013年3月. [China-Pub]
(I acknowledge Prof. Jeffrey D. Ullman and Dr. Jure Leskovec for allowing me to use their slides, and to make some modifications if necessary. )
Introduction: Data-Intensive Scalable Computing (DISC); Data Mining
24/04/2013 MapReduce and Hadoop HA Ch.1 - 3
Frequent Itemsets and Association Rules
MMDS Ch.6; DM Ch.5
28/04/2013 Form groups, and send the group information to TA. Deadline: 23:59pm
Dimensionality Reduction and Matrix Factorization
ref1; Math Basics
08/05/2013 Project 1 due. Deadline: 23:59pm [Project 1]
Recommender Systems MMDS Ch.9
Link Analysis: PageRank, Hubs and Authorities, Spam Detection MMDS Ch.5 22/05/2013
Unsupervised Learning: Clustering
Supervised Learning: Perceptron,Naive Bayes,kNN,SVM
Finding Similar Items: Minhashing and Locality-Sensitive Hashing
MMDS Ch.3 07/06/2013
Mining Data Streams
MMDS Ch.4 14/06/2013
02/07/2013 Project 2 due. Deadline: 23:59pm [Project 2]
data structure, design and analysis of algorithms, linear algebra, probability theory
1. Class attendance (10%)
2. Homework (20%)
3. Final exam (40%)
4. Project (30%)
Assignments turned in late will be penalized 20% per late day.
Honesty and integrity are central to the academic work. All your submitted assignments must be entirely your own (or your own group's). Any student found cheating or performing plagiarism will receive a final score of zero for this course.