October

Week 7

Tuesday 5 October

  • quiz 3: DM ch4 (4.6 to end of chapter) & pg2DM (open note)
  • starting pg2DM ch5
  • final project written proposal due (1-2 pages) (50XP)
  • presentation of final project proposal to class (50XP)

Thursday 7 October

  • Home Practice #4: Do the three tasks on page 4-19 of pg2DM. Submit both the code and the answers to the questions to submit.o.bot@gmail.com (subject: 470: home practice #4)
  • finish presentations
  • pick proposal to work on and organize into teams

Week 8

Tuesday 12 October

  • Fall Break

Thursday 14 October

  • FINAL EXAM questions start appearing
  • Worksheet 5. Linear Regression
  • Initial SCRUM meeting.
  • Team Presentation: DM ch5
  • visualization

Week 9

Tuesday 19 October

  • SCRUM meeting – 5min.
  • finish team presentation of ch 5
  • git video
  • git lab
  • A bit of a review
    • Bayes
    • Lift
    • Precision and Recall
    • Linear Regression

Thursday 21 October

  • combined quiz 3&4: DM ch4 (4.6 to end), ch 5 & Bayes Formula. Know the following:
    • Bayes Formula – know the formula in the middle of p90 for P(h|e) and be able to use it!
    • know the algorithms (the ones in boxes)  given in 4.6 to the end
    • when would you use linear regression? perceptions? winnow?
    • what are their disadvantages?
    • Euclidean distance
    • Instance based learning
    • clustering, incl. nearest neighbor, k means, kD trees and ball trees.
    • know what recall and precision are as well as false positives and false negatives
    • Know when you would use ROC Curves, Recall-Precision Curves, Lift Charts, Cross Validation
    • What is the most realistic evaluation method for clustering? For other data mining methods?
  • team many-eyes.com demo

Week 10

Tuesday 26 October

Thursday 28 October

  • FINAL EXAM first questions.  Complete and submit via submit.o.bot by Nov 4 for 10% bonus XP
  • regression worksheet.
  • Data Visualization
  • at least 1/2 hr. to work on team projects.