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

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

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

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

3 
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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 
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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]

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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 
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12 
Frequent pattern mining: Beyond itemset
[pdf]
[bookchapter07]
[bookchaptersequentialpattern]
[bookchaptergraphpattern]

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

7 
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Midterm exam
[pdf]
[solutions]

8 
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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 
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Fall break

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

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16 
Classification: Evaluation
[pdf]

10 
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17 
Classification: Ensembles
[pdf]
[TypingDemo.zip (Ensembles)]

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18 
Classification: SVM
[pdf]
[SVMDemo.zip]

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19 
Classification: Neural networks
[pdf]

12 
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20 
Clustering: Concepts
[pdf]

12 
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21 
Clustering: Partitioning methods
[pdf]
[kmeansdemo.zip]  HW4 due [solutions], HW5 out [pdf]

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22 
Clustering: Kernelbased clustering
[pdf]

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23 
Clustering: Densitybased clustering [pdf]

14 
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24 
Clustering: Evaluation [pdf]

14 
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Thanksgiving break

15 
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25 
Course review 2, HW5 due [solutions]

15 
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26 
Course review 3, Project due

16 
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27 
Project presentation 1

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28 
Project presentation 2

17 
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Final exam [sample exam paper]
