DS4140-CS5140-CS6140

DS4140/CS5140/CS6140 Data Mining

Class Hours: Monday/Wednesday 3:00pm-4:20pm, IVC (Zoom Link on Canvas)

Instructor

Qingyao Ai (aiqy[at]cs[dot]utah[dot]edu)

Office Hours: Monday 4:30pm-5:30pm, IVC (Zoom Link on Canvas)

TA/TM

• Assignments and Grading
  • Hasan Pourmahmoodaghababa
    • h[dot]pourmahmoodaghababa[at]utah[dot]edu
    • Office Hours: Thursday 12:00pm-2:00pm, IVC (Zoom Link on Canvas)
  • Hansi Zeng
    • hanszeng[at]cs[dot]utah[dot]edu
    • Office Hours: Tuesday 3:30pm-5:30pm, IVC (Zoom Link on Canvas)
• Project Management
  • Zhenduo Wang
  • zhenduow[at]cs[dot]utah[dot]edu
  • Office Hours: appointments only

Prerequisites

• Basic Probability, Linear Algebra, Big-O Analysis
• Basic Programming and Data Structures

Text Books:

• [M4D] Mathematical Foundations for Data Analysis. Jeff M. Phillips.
• [MMDS] Mining of Massive Datasets. Anand Rajaraman, Jure Leskovec, and Jeff Ullman. (optional)
• [FoDS] Foundations of Data Science. Avrim Blum, John Hopcroft and Ravindran Kannan. (optional)

Resources

• Previous Class Website. Jeff M. Phillips.
• Previous Class Syllabus. Jeff M. Phillips.
• Previous Class Project Description. Jeff M. Phillips.

Grading

The grade will count the assessments using the following proportions (tentative and subject to change):

• 40% of your grade will be determined by homework:
  • We will have 8 short homework assignments roughly covering each main topic in the class
  • Each assignment take 5% of the final grade.
• 25% of your grade will be determined by the class project.
  • 5% of your grade will be determined by the project proposal.
  • 5% of your grade will be determined by the intermediate report.
  • 5% of your grade will be determined by the project poster presentation.
  • 10% of your grade will be determined by the project final report.
• 10% of your grade will be determined by the paper presentation.
• 10% of your grade will be determined by Mid-term Exam.
• 10% of your grade will be determined by Final Exam.
• 5% of your grade will be determined by your participation. Students who finish the class project, peer reviews, and all assignments/exams will receive the full credits of participation.

Tentative Class Schedule

Week, Date	Topic	Textbook and Resources
Week 01, 01/20	Class Overview
Week 02, 01/25	Statistics Principles	M4D 2.2-2.3, MMDS 1.2, FoDS 12.4
Week 02, 01/27	Similarity : Jaccard + k-Grams	M4D 4.3-4.4, MMDS 3.1+3.2, FoDS 7.3
Week 03, 02/01	Similarity : Min Hashing	M4D 4.6.6, MMDS 3.3
Week 03, 02/03	Similarity : LSH	M4D 4.6, MMDS 3.4
Week 04, 02/08	Similarity : Distances	M4D 4-4.3, MMDS 3.5+7.1, FoDS 8.1
Week 04, 02/10	Similarity : Word Embed + ANN vs. LSH	M4D 4.4, MMDS 3.7+7.1.3
Week 05, 02/15	Presidents Day
Week 05, 02/17	Clustering : Hierarchical	M4D 8.5+8.2, MMDS 7.2, FoDS 7.7
Week 06, 02/22	Clustering : K-Means	M4D 8-8.3, MMDS 7.3, FoDS 7.2-3
Week 06, 02/24	Clustering : Spectral	M4D 10.3, MMDS 10.4, FoDS 7.5
Week 07, 03/01	Streaming : Model and Misra-Greis	M4D 11.1-11.2.2, FoDS 6.2.3, MMDS 6
Week 07, 03/03	Streaming : Count-Min Sketch, Count Sketch, and Apriori	M4D 11.2.3-4, FoDS 6.2.3, MMDS 4.3
Week 08, 03/08	Regression : Basics, and Ridge Regression	M4D 5-5.3
Week 08, 03/10	Regression : Lasso + MP + Comp. Sensing	M4D 5.5, FoDS 10.2
Week 09, 03/15	Regression : Cross-Validation and p-values	M4D 5.5
Week 09, 03/17	Mid-term Exam
Week 10, 03/22	Dim Reduce : SVD + PCA	M4D 7-7.3+7.5, FoDS 4
Week 10, 03/24	Dim Reduce : Matrix Sketching	M4D 11.3, MMDS 9.4, FoDS 2.7+7.2.2
Week 11, 03/29	Dim Reduce : Metric Learning	M4D 7.6-7.8
Week 11, 03/31	Noise : Random Projections and Noise in Data	M4D 7.10+8.6, MMDS 9.1, FoDS 2.9
Week 12, 04/05	Non-Instructional Day
Week 12, 04/07	Noise : Calibration and Counterfactual Learning	Cascade Model Craswell et al. (WSDM 2008) DBN Chapelle and Zhang (WWW 2009) UBM Dupret and Piwowarski (SIGIR 2008) IPW Joachims et al. (WSDM 2017) Regression EM Wang et al. (WSDM 2018) DLA Ai et al. (SIGIR 2018)
Week 13, 04/12	Graph Analysis : Markov Chains	M4D 10.1, MMDS 10.1+5.1, FoDS 5
Week 13, 04/14	Graph Analysis : PageRank	M4D 10.2, MMDS 5.1+5.4
Week 14, 04/19	Graph Analysis : MapReduce or Communities	M4D 10.4, MMDS 2+10.2+5.5, FoDS 8.8+3.4
Week 14, 04/21	Class Project Presentation
Week 15, 04/26	Final Exam

Acknowledgements

Special thanks to Prof. Jeff M. Phillips from University of Utah. Teaching materials are borrowed from his courses on CS5140/6140: Data Mining.