1 
0116 (T) 
1 
Introduction

1 
0118 (R) 
2 
Data preprocessing: Data description (HW1 out)

2 
0123 (T) 
3 
Data preprocessing: Data visualization (Last date for class change)

2 
0125 (R) 
4 
Data preprocessing: Data cleaning and data integration

3 
0130 (T) 
5 
Data preprocessing: Data reduction and dimension reduction

3 
0201 (R) 
6 
Classification: Concepts and decision trees model

4 
0205 (M) 
 
Project: Proposal paper due

4 
0206 (T) 
7 
Project: Teaming and proposal (HW1 due and HW2 out)

4 
0208 (R) 
8 
Classification: Naive Bayes model and Bayesian networks

5 
0213 (T) 
9 
Classification: Evaluation

5 
0215 (R) 
10 
Classification: Ensembled methods

6 
0220 (T) 
11 
Classification: Support Vector Machines (HW2 due)

6 
0222 (R) 
12 
Classification: Artificial neural networks

7 
0227 (T) 
13 
Course review 1 and HW1/HW2 feedback

7 
0301 (R) 
 
Midterm exam

8 
0306 (T) 
14 
Exam feedback and project QA

8 
0307 (W) 
 
Project: Milestone paper due

8 
0308 (R) 
15 
Project: Milestone presentations

10 
0320 (T) 
16 
Clustering: Concepts (HW3 out)

10 
0322 (R) 
17 
Clustering: Partitioning methods

11 
0327 (T) 
18 
Clustering: Hierarchical, densitybased, and kernelbased clustering

11 
0329 (R) 
19 
Clustering: Evaluation

12 
0403 (T) 
20 
Data Science talk 1 (HW3 due and HW4 out)

12 
0405 (R) 
21 
Frequent pattern mining: Concepts and Apriori

13 
0410 (T) 
22 
Frequent pattern mining: FPGrowth

13 
0412 (R) 
23 
Frequent pattern mining: Evaluation

14 
0417 (T) 
24 
Frequent pattern mining: Beyond itemsets

14 
0419 (R) 
25 
Data Science talk 2 (HW4 due)

15 
0424 (T) 
25 
Course review 2 and HW3/HW4 feedback

15 
0426 (R) 
26 
Project: Oral presentations and QA

16 
0501 (T) 
27 
Project: Poster presentations

16 
0503 (R) 
 
Project: Term paper due

17 
0508 
 
Final exam (10:30AM  12:30PM)
