Schedule

Week 1

Thursday January 15

• Course Logistics
  • syllabus review
• Intro
  • distance measures
  • recommendation systems
  • sample RAY
• Portfolio Assignment 1 (due January 24th)

Week 2

Thursday January 22

• Data mining examples
  • language processing
  • grocery stores
• RAT 2 (~ 12-15 multiple choice questions)
  • DM ch 2-3
  • PCI - ch 1-2 (I will not ask questions about Python)
  • One 3x5 card (both sides) of notes allowed
• Python intro
  • We will spend time in class getting the delicio.us example to work. This requires loading libraries and I just want to make sure everyone is up to speed on this.
• Pearson's Correlation Coefficient
  • Worksheet ( data spreadsheet)
• Portfolio Assignment 2 (due February 1)

Week 3

Thursday January 29

• we will talk about PCI ch 2. and work through some examples on the k-nearest neighbor recommendation system.
• we will also look at recommendation systems that do not use collaborative filtering.
• we will cover DM ch 4 through section 4.3 (the one on decision trees) ( PDF of slides)
• we will take some time to talk about the Python work we have been doing.

Week 4

Thursday - February 5

This week is focused on seeking happiness and understanding. That means we will make sure all the big conceptual things are clear. And all the nasty Python confusions are cleared up.

New material includes the nuts and bolts of decision trees and rule based systems.

Week 5

Thursday - February 12

• RAT 3 on chapter 4.
• visualization
  • video
  • http://www.babynamewizard.com/voyager
  • http://www.many-eyes.com
  • http://benfry.com/zipdecode/
• pci demos
• finish weka worksheet
• ?

Week 6

Thursday - February 19

• Team Demo - many-eyes.com
• PCI- chapter 3 - clustering and visualization

Week 7

Thursday - February 26

• imdb challenge
• visualization discussion

Week 8

Thursday - 5 March

Break

Week9

Thursday - 12 March

I have a whopper cold and am operating at about 3 out of 8 cylinders. I will be @ class but not at high energy.

• Visualization presentations.
• I will present an introduction to information retrieval. This material is not in either book. There will also be an accompanying worksheet.
• Movie progress reports.
• Discussion on whether we can multitask - work on the movie system and also do the IR chapter of the PCI book.
• Discussion of what should be on RAT 4.
  • PCI ch3 & 4.
  • chapter from IR online textbook.
• Perhaps watch of video related to IR and data mining by Peter Norvig.

Week 10

Thursday - 19 March

• Genetic Algorithms ( slides)