1 
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1 
Introduction (by Prof. Nitesh Chawla)
[pdf]
[bookchapter01]
[pdflecture]

1 
082417 (R) 
2 
Data Processing: Data description (by Keith Feldman)
[pdf]
[bookchapter02]
[pdflecture]
[pdfnotebook]
[ipynb]

2 
082917 (T) 
3 
Data Processing: Data visualization (by Keith Feldman)
[pdf]
[pdflecture]
[pdfnotebook]
[ipynb] 
HW1 out [pdf]

2 
083117 (R) 
4 
Data processing: Data cleaning and data integration (by Keith Feldman)
[pdf]
[bookchapter03]
[pdflecture]
[pdfnotebook]
[ipynb]

3 
090517 (T) 
5 
Data processing: Data reduction and dimension reduction (by Keith Feldman)
[pdf]
[pdflecture]
[pdfnotebook]
[ipynb]

3 
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6 
Frequent pattern mining: Apriori (by Keith Feldman)
[pdf]
[bookchapter06]
[pdflecture]

4 
091217 (T) 
7 
Project introduction [pdflecture (updated Sept. 29)], Project out [pdfinstruction (updated Sept. 29)]  HW1 due [solutions] [plots], HW2 out [pdf]

4 
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8 
Data cube: Concepts and operations
[pdf]
[bookchapter05]

5 
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9 
Data cube: Data warehouse and OLAP [pdf]
[bookchapter04]

5 
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10 
Frequent pattern mining: FPGrowth
[pdf]  HW3 out [pdf]

6 
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11 
Frequent pattern mining: Evaluation
[pdf]  HW2 due [solutions]

6 
092817 (R) 
12 
Frequent pattern mining: Beyond itemset
[pdf]
[bookchapter07]
[bookchaptersequentialpattern]
[bookchaptergraphpattern]

7 
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13 
Course review 1 [ResponseBotDemo.zip (data, code, notebook)] [pdf]  HW3 due [solutions]

7 
100517 (R) 
 
Midterm exam
[pdf]
[solutions]

8 
101017 (T) 
14 
Classification: Decision tree induction
[pdf]
[bookchapter08]
[DecisionTreeDemo.zip (data, code)]

8 
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15 
Classification: Naive Bayes [pdf] [paperclassification.zip (Project Demo)]  HW4 out [pdf]

9 
101717 (T) 
 
Fall break

9 
101917 (R) 
 
Fall break (Let's Go Irish! Beat Trojans!)

10 
102417 (T) 
16 
Classification: Evaluation

10 
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17 
Classification: Ensembles

11 
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18 
Classification: SVMs

11 
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19 
Classification: Neural networks

12 
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20 
Clustering: Concepts

12 
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21 
Clustering: Partitioning methods  HW4 due, HW5 out

13 
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22 
Clustering: Kernelbased clustering

13 
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23 
Clustering: Densitybased clustering

14 
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24 
Clustering: Evaluation

14 
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Thanksgiving break

15 
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25 
Course review 2, HW5 due

15 
113017 (R) 
26 
Course review 3, Project due

16 
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27 
Project presentation 1

16 
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28 
Project presentation 2

17 
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Final exam
