I use Moodle for my courses, which makes it challenging to make my
course content public. The website you see below is an automatically
generated approximation of my Moodle page, based on a script that I've
run.
General materials
- Syllabus
- Textbook: Mining Massive Datasets, version 1.2
- Clicker questions
Week 1
- Warmup
- Due Mon Apr 01 23:55:00 CDT 2013
- Data summarization
- Due Wed Apr 03 23:55:00 CDT 2013
- k-NN, part 1
- Due Fri Apr 05 23:55:00 CDT 2013
Week 2
- k-NN, part 2
- Due Mon Apr 08 23:55:00 CDT 2013
- Locality Sensitive Hashing, part 1
- Due Thu Apr 11 23:55:00 CDT 2013
Week 3
- Locality Sensitive Hashing, part 2
- Due Mon Apr 15 23:55:00 CDT 2013
- Naive Bayes Classifier
- Due Thu Apr 18 23:55:00 CDT 2013
Week 4
- PageRank, part 1
- Due Mon Apr 22 23:55:00 CDT 2013
- PageRank, part 2
- Due Thu Apr 25 23:55:00 CDT 2013
Week 5
- Exam 1 topics
- Exam 1: Wednesday, May 1. Bring a calculator.
- Association rules, part 1
- Due Thu May 02 23:55:00 CDT 2013
Week 6
- Association rules, part 2
- Due Wed May 08 23:55:00 CDT 2013
Week 7
- K-Means Clustering, part 1
- Due Wed May 15 23:55:00 CDT 2013
- K-Means Clustering, part 2
- Due Fri May 17 23:55:00 CDT 2013
Week 8
- Final project proposal
- Due Sun May 19 23:55:00 CDT 2013
- Agglomerative Clustering
- Due Thu May 23 23:55:00 CDT 2013
Week 9
- Recommender systems
- Due Wed May 29 23:55:00 CDT 2013
- Paper on Local Outlier Factor
Week 10
- Exam 2 topics
- Exam 2: Wednesday, June 5