Lecture schedule

Lecture 1 Introduction to data mining

Lecture 2 K-Nearest neighbor (KNN) algorithm

Lecture 3 Data preprocessing

Lecture 4 Decision Tree algorithms

Lecture 5 Ensemble algorithms

Lecture 6 Model/performance evaluation

Lecture 7 SVM

Lecture 8 Neural networks

Lecture 9 Deep learning

-----> Midterm (after fall break)

-----> Final project proposal Due

Lecture 10 Clustering

Lecture 11 Frequent itemset mining/rule minig

Lecture 12 Regression

Lecture 13 Symbolic regression

Lecture 14 Text/web mining

Lecture 15 Time series mining

Lecture 16 Graph mining

Lecture 17 Graph neural networks

Lecture 18 Course Summary

-----> Final project presentation (exam day)