(Fall 2023) CS484: Data Mining

Instructor: Mingrui Liu

Description

Time/Location

• Tuesday/Thursday 12:00pm—1:15pm, Exploratory Hall Room L102

Office Hours:

• Monday 2pm—3pm or by appointment

Contact Information:

• Office: Research Hall 355

• Email: (firstname)l (at) gmu (dot) edu

Recommended Textbooks:

• Tan, Steinbach, Karpatne and Kumar. Introduction to Data Mining (Second Edition)

Tentative Schedule

• Week 1 (Aug 22, Aug 24): Introduction to Data Mining and Course Overview

• Week 2 (Aug 29, Aug 31): Classification Basics and K nearest neighbor

• Week 3 (Sep 5, Sep 7): Principal Component Analysis

• Week 4 (Sep 12, Sep 14): Decision Tree and Overfitting

• Week 5 (Sep 19, Sep 21): Linear Regression

• Week 6 (Sep 26, Sep 28): Logistic Regression and Neural Networks

• Week 7 (Oct 3, Oct 5): Deep Learning

• Week 8 (Oct 10, Oct 12): Oct 10 does not meet, Oct 12 midterm

• Week 9 (Oct 17, Oct 19): Clustering

• Week 10 (Oct 24, Oct 26): Clustering, Association Rule Mining

• Week 11 (Oct 31, Nov 2): Association Rule Mining, Recommendation Systems Basics

• Week 12 (Nov 7, Nov 9): Recommendation Systems Basics

• Week 13 (Nov 14, Nov 16): Advanced Recommendation Systems

• Week 14 (Nov 21, Nov 23): Project Presentation, Nov 23 thanksgiving break

• Week 15 (Nov 28, Nov 30): Project Presentation

• Week 16 (TBD): Final Exam

Grading Policy

Grades:

• A: greater than 93

• A-: [90, 93)

• B+: [87, 90)

• B: [83, 87)

• B-: [80, 83)

• C+: [77, 80)

• C: [73, 77)

• C-: [68, 73)

• D: [60, 68)

• Fail: below 60

Weights:

• Homework: 40% (5 assignments in total)

• Midterm Exam: 15%

• Final Exam: 25%

• Project: 20%

Late penalty:

• Late submissions for whatever reason will be punished. 5% of the score of an assignment/project will be deducted per day with maximum tolerance 3 days. For example, if an assignment is submitted 2 days and 1 minute later than the deadline (counted as 3 days) and it gets a grade of 90%, then the score after the deduction will be: 95% - 3*5% = 80%.

Honor Code