Schedule

Date   Description Bibliography
Slides
5/09/2017   What is data mining on the Web/ Introductory lecture

Chapter 1, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

8/09/2017   Map-Reduce Framework Chapter 2, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
12/09/2017   Map-Reduce Framework

Chapter 2, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

15/09/2017   Frequent itemsets and Association rules

Chapter 6, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

19/09/2017   Frequent itemsets and Association rules Chapter 6, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
22/09/2017   Finding Similar Items

Chapter 3, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

26/09/2017   Finding Similar Items Chapter 3, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
29/09/2017   Clustering

Chapter 7, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

  • Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data clustering: a review. ACM Comput. Surv. 31, 3 (Sep. 1999), 264-323.
03/10/2017   Clustering

Chapter 7, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

 

06/10/2017   Recommendation Systems

Chapter 9, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

10/10/2017   Dimensionality Reduction

Chapter 9, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

13/10/2017  

Midterm

Chapters: 1, 2, 3, 6, 7, 9

17/10/2017   Link Analysis and Web search

Chapter 5,Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Optional Reading:

20/10/2017   Link Analysis and Web search

Chapter 5, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

24/10/2017   Mining Data Streams Chapter 4, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
27/10/2017   Advertising on the Web Chapter 8, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
31/10/2017   Learning through Experimentation 

Chapter 8, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014

Multi-armed bandit problem (wikipedia)

A Contextual-Bandit Approach to Personalized News Article Recommendation by Li, Chu, Langford, Schapier. WWW 2010.

 

03/11/2017   Large-Scale Machine Learning Chapter 12, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014
07/11/2017   Mining Social-Network Graphs Chapter 10, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Lecture 19
10/11/2017   Use case: Sampling and filtering on IoT Devices  "AdaM: an Adaptive Monitoring Framework for Sampling and Filtering on IoT Devices", D. Trihinas and G. Pallis and M. D. Dikaiakos, "2015 IEEE International Conference on Big Data" (IEEE BigData 2015), Santa Clara, CA, USA 2015.
14/11/2017   Use case: Monitoring and Detecting Online Hate Speech from Twitter

Mandola project publications

 

21/11/2017   People, Computers, and The Hot Mess of Real Data

Joe Hellerstein: Professor / University of California, Berkeley

KDD 2016 Keynote Lecture 22
24/11/2017   Projects Presentation  
28/11/2017   Projects Presentation  
1/12/2017   Revision/ Q&A