Instructor: Mingrui Liu
Time/Location
Office Hours:
Contact Information:
Office: Research Hall 355
Recommended Textbooks:
Tan, Steinbach, Karpatne and Kumar. Introduction to Data Mining (Second Edition)
Week 1 (Aug 26, Aug 28): Introduction to Data Mining and Course Overview
Week 2 (Sep 2, Sep 4): Classification Basics and K nearest neighbor
Week 3 (Sep 9, Sep 11): Principal Component Analysis
Week 4 (Sep 16, Sep 18): Decision Tree and Overfitting
Week 5 (Sep 23, Sep 25): Linear Regression
Week 6 (Sep 30, Oct 2): Logistic Regression and Neural Networks
Week 7 (Oct 7, Oct 9): Deep Learning
Week 8 (Oct 14, Oct 16): Oct 14 (Midterm), Clustering
Week 9 (Oct 21, Oct 23): Clustering
Week 10 (Oct 28, Oct 30): Association Rule Mining
Week 11 (Nov 4, Nov 6): Nov 4 does not meet (Election Day), Recommendation Systems Basics
Week 12 (Nov 11, Nov 13): Recommendation Systems Basics
Week 13 (Nov 18, Nov 20): Advanced Recommendation Systems
Week 14 (Nov 25, Nov 27): Project Presentation, Nov 27 thanksgiving break
Week 15 (Dec 2, Dec 4): Project Presentation
Week 16 (Dec 11): Final Exam (10:30am--1:15pm, classroom)
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%.