Class Schedule

Week 1

 Monday 13 January

• what is data mining
• course logistics
• a simple movie rating data set

Wednesday 15 January

• collaborative filtering
• nearest neighbor algorithm

Friday 17 January

•  Pearson Correlation Coefficient
• athletes.xls
• team worksheet
• data.xls

Week 2

 Monday 20 January

• no class- Martin Luther King Holiday

Wednesday 22 January

• snow day

Friday 24 January

• RAT 1: Chapters 1 & 2
• Team Task: demo recommender.py with the book database
• Partner Task: recommendation system for movie ratings 
• possible presentations. (please gmail me (ron.zacharski) if you are interested.

Week 3

 Monday 27 January

• Partner Task II: Movie Lens Task
• demo Movie Lens Recommendation System.
• HOMEWORK: Finish Movie Lens Task

Wednesday 29 January

• In the news (will show a sampling of these):
• Amazon ships before you order
• IBM Watson
• Low energy Bluetooth
  • What is it?
  • Where are you?
  • Health care
• Imagine the Future of Aging
• The Birth of a Word
• Movie Lens cont’d

Friday 31 January

• work on the wine dataset
  • zip file containing code to get started
• Movie Lens revisited.

Week 4

 Monday 3 February

• RAT 2: Chapter 3 (one 3×5 card of notes –can use both sides)

Wednesday 5 February

• Announcements:
  • No class 28th February – the day before spring break (I will be in New Mexico).
  • Hackathon(s)?
  • Presentation reminder.
• Summary: Here is what we covered so far.
• Weighted Slope One Worksheet
• Chapter 3 Coding Practice 

Friday 7 February

• Finish up tasks (worksheet  & programming)

Week 5

 Monday 10 February

• research talk: Arabic Classification

Wednesday 12 February

• Decision Trees

Friday 14 February

• Decision Tree Lab

Week 6

 Monday 17 February

• RAT 3: Chapter 4 (one 3×5 card of notes)

Wednesday 19 February

• Decision Trees cont’d 
• Optional HW: create a decision tree for the contact lens data

Friday 21 February

• Lab to finish ch 3 tasks.

Week 7

 Monday 24 February

• Covering Rules
• Ch 4 lab.

Wednesday 26 February

Friday 28 February

 

Week 8

Spring Break

Week 9

 Monday 10 March

• dimensionality
• finish ch 4 lab
  • single attribute classifier

Wednesday 12 March

• RAT 4: ch 5
• Chapter 5 Tasks

Friday 14 March

Week 10

 Monday 17 March

• snow day

Wednesday 19 March

• Data Set Challenge. Can you create a classifier and evaluate its performance for the Wisconsin Breast Cancer Data Set (using wdbc.data and names)? 75XP
• Linear Regression.
  • online regression calculator  (you try on your data 25xp)
  • sample house data
  • blog post about a simple regression method  (implement for 100xp)

Friday 21 March

 

Week 11

 Monday 24 March

• RAT 5: Chapter 6
• Bayes Team worksheet

Wednesday 26 March

• Presentation: Practical Applications of Bayes: Allie Cropp
• More on accessing the accuracy of classifiers (FN & FP)
• FP & FN partner task
  • calculate the FP & FN for a few classifiers on the Pima data and/or the cancer data
  • recall that the diabetes data is if the person gets diabetes in 3 years. The cancer data is if the tumor is malignant or benign
  • for these 2 data sets which (if any) measure is more important?
• Bayes Work – Chapter 6 tasks

Friday 28 March

• Lift Charts
• Bayes Work cont’d

Week 12

 Monday 31 March

• Information Retrieval
• Tokenization and Normalization
• term document incidence matrices
• inverted index (postings index)

Wednesday 2 April

• Information Retrieval review
• term frequency
• document frequency
• collection frequency
• inverse document frequency (IDF)
• log likelihood ratio
• Google Ngram viewer

Friday 4 April

• 2 quick videos
  • Australian TV’s look at Data Mining & Marketing
  • Future of  cognitive computing
• Neural Network approaches to machine learning
• lab

Week 13

 Monday 7 April

• RAT 6 Chapter 7 
• Cognitive Computing TED talk
• Building Watson
• Chapter 7 tasks
• umw Twitter dataset

Wednesday 9 April

• more on naive bayes w/ text
• computers read the web

Friday 11 April

• Read the Web – David Heller

Week 14

 Monday 14 April

• FINAL EXAM
• clustering
  • dog distances sorted (20xp)
  • people.csv (30xp)
  • rough clustering code: cluster.py
  • dogs2.csv
•  Graph Theory and mining the web: Benjamin Blalock.

Wednesday 16 April

• Fighting Fires with Data Mining – Annika Lewis
• Hands on clustering
• new version of clusterer
• Breakfast cereal data
• New Mexico zip code data

Friday 18 April

•  Data Mining in the Medical Field – Allie Cropp
• k-means clustering
• kmeans.py

 

Week 15

 Monday 21 April

• Spatial Data Mining – Sean Healy
• Clustering unstructured text
  • clusterText.py
  • tiny.zip (a trivial example)
  • 100 xp to finish the clustering python script

Wednesday 23 April

• Clustering: a summary
• Melanie Mitchell on Complex Systems (check out her free online courses at the Santa Fe Institute!!)
• More on complexity – a playlist

Friday 25 April

• GraphLab – a presentation by Elizaebeth Greene and Erik Nosar