Lectures

The readings consist mainly of the chapters from the textbook supplemented with sections from other reference books (referenced by the lastname of the first author) and papers.

Date Topics and Slides/Notes Readings/Hw
01/17  Snow day
01/22 Introduction Ch 1
01/24 Data exploration Ch 2
01/29 Dimension reduction - PCA and SVD MMDS Ch 11
01/31 Frequent Itemset Mining Ch 6
Hw1
02/05
02/07 Truth discovery using crowdsourced data (Daniel Garcia-Ulloa) VLDB '17
02/12 Classification: basic concepts Ch 8
Hw2
02/14
02/19
02/21 Classification: advanced methods Ch 9
02/26
02/28
03/05 Clustering: concepts and basic methods Ch 10
03/07
03/19 Clustering: advanced methods Ch 11
03/21 Midterm exam (review)  
03/26 Outlier Detection Ch 12
03/28 Recommender Systems MMDS Ch 9
04/02 Recommender Systems
04/04 Link Analysis MMDS Ch 10
04/09 Privacy preserving data mining  
04/11 Spatiotemporal data mining: trajectory prediction
04/16    
04/18    
04/23 Project workshop
04/25 Project workshop
04/30 Project workshop
05/07 Final exam (8am) (review)