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IST 557 Data Mining: Techniques and Applications

 

  • all homework/reports are due at midnight (11:59pm)
  • schedule is subject to change
  • black: lectures; blue: in-class discussions; orange: student presentation

 

 

Week

Date

Lecture (tentative schedule)SlidesDeadline

1

8/27

Course overview

Intro to data mining

0-class-intro.pdf

quiz

quiz due 11:59pm 8/28 Wednesday

 

8/29

Intro to classification

 

1-classification-intro.pdf

8/31 regular drop deadline

9/1 add deadline

2

9/3

Classification 1 - decision tree2-decision-tree.pdf 

 

9/5

Classification 1 - bagging & boosting

3-bagging.pdf

4-boosting.pdf

Assign 1 

3

9/10

Assign 1 Q&A (TA in class) Assign 1 due 11:59pm 9/10 Tuesday

 

9/12

Classification 2 - perceptron, SVM

5-perception.pdf

6-svm.pdf

 

4

9/17

Classification 2 - Kernel7-kernel.pdfAssign 2

 

9/19

Classification 3 - bayesian statistics

 

 

5

9/24

Assign 2 Q&A (TA in class)

 Assign 2 due 11:59pm 9/24 Tuesday

 

9/26

Classification 4 - generative models

  

6

10/1

Classification 5 - logistic regression Assign 3

 

10/3

Classification 6 - deep learning

  

7

10/8

Clustering 1  

 

10/10

Clustering 2

 Assign 4

8

10/15

Clustering 3

  

 

10/17

advanced topic 1

  

9

10/22

advanced topic 2

 Project starts

 

10/24

advanced topic 3

  

10

10/29

project check point 1 project check 1

 

10/31

advanced topic 4

  

11

11/5

project check point 2 project check 2

 

11/7

invited lecture from industry  

12

11/12

project check point 3 project check 3

 

11/14

invited lecture from industry

 11/15 late drop deadline

13

11/19

project check point 4 (midterm)

 project check 4 midterm

 

11/21

invited lecture from industry

  
1411/24-30Thanksgiving week  

15

12/3

Project lab  

 

12/5

Project lab  

16

12/10

project check point 5 (final) project check 5 final

 

12/12

Report writing  

 


 


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