CSCI 4380/6380: Data Mining (Spring 2023)
Course Information
Instructor: Dr. Ninghao Liu
Course time and location:
TR: 3:55 pm - 5:10 pm, Miller Plant Sci 2102
W: 4:10 pm - 5:00 pm, Forest Resources-1 0304
Office hours: Thursday, 11:00 am - 11:59 am
Office: Boyd 616
TA: TBD
Course Description
The goal of this course is deriving a comprehensive understanding of fundamental issues, techniques, applications and future directions of data science and data mining. This course presents a rigorous overview of methods for machine learning, dimension reduction, modeling methods for tabular data, texts and graphs, and industry applications including outlier detection and recommender systems.
Textbooks
Data mining is a highly interdisciplinary and fast-growing field, especially driven by the recent advances of machine learning and deep learning. We will heavily rely on course slides in class.
In case students are interested, the textbooks (not required) for this course are:
“Data Mining: Concepts and Techniques, 3rd edition” by Jiawei Han, Micheline Kamber, Jian Pei.
“Learning From Data” by Yaser S.Abu-Mostafa.
“Introduction to Information Retrieval” by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze.
Course Prerequisite (Important)
Students are expected to have a working knowledge of Python. All programming assignments must be completed using Python unless it is specified otherwise. Preliminary knowledge of calculus, statistics and linear algebra are required.
Grading
| Letter Grade | A | A- | B | B | B- | C | C | C- | D | F |
| Range | [90, 100] | [87, 90) | [84, 87) | [80, 84) | [77, 80) | [74, 77) | [70, 74) | [67, 70) | [60, 67) | [0, 60) |
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Late Submission Policy: For homework assignments, 20% is deducted for each late day for up to 48 hours (including weekends) after which submissions are not accepted. Late presentation materials and project reports not accepted.
Exams: Exams are open-notes.
Academic Honesty
We will strictly follow UGA鈥檚 Academic Honesty Policy. Dishonest behavior will not be tolerated and may result into failing the course. Please contact the instructor if you have any concerns regarding this issue.
Course Schedule (Tentative)
| Week | Date | Topic | Notes |
| 1 | 01/10 | Course Overview | |
| 01/11 | Classification: kNN | |
| 01/12 | Classification: Linear models | |
| 2 | 01/17 | Classification: Linear models | HW1 out |
| 01/18 | Classification: Multi-class Classification | |
| 01/19 | Classification: Evaluation | |
| 3 | 01/24 | Tabular data mining | |
| 01/25 | Tabular data mining | |
| 01/26 | Text mining: Preliminaries | Form Teams for Project |
| 4 | 01/31 | Text mining: Vector space model | |
| 02/01 | Text mining: Vector space model | |
| 02/02 | Graph mining: Preliminaries | |
| 5 | 02/07 | Graph mining | HW1 due, HW2 out |
| 02/08 | Graph mining | |
| 02/09 | Machine learning: Overfitting and Regularization | |
| 6 | 02/14 | Machine learning: Overfitting and Regularization | |
| 02/15 | Classification: Naive Bayes classifiers | |
| 02/16 | Classification: Naive Bayes classifiers | |
| 7 | 02/21 | Classification: Decision Tree | |
| 02/22 | Classification: Decision Tree | HW2 due, HW3 out |
| 02/23 | Clustering | |
| 8 | 02/28 | Clustering | |
| 03/01 | Clustering evaluation | |
| 03/02 | Midterm Exam | |
| 9 | 03/07 | - | Spring Break. No class. |
| 03/08 | - | Spring Break. No class. |
| 03/09 | - | Spring Break. No class. |
| 10 | 03/14 | Applications: Outlier detection | |
| 03/15 | Applications: Outlier detection | |
| 03/16 | Applications: Recommender systems | |
| 11 | 03/21 | Applications: Recommender systems | |
| 03/22 | Applications: Recommender systems evaluation | |
| 03/23 | Text mining: Embedding | |
| 12 | 03/28 | Text mining: Embedding | |
| 03/29 | Text mining: Attention Mechanism | HW3 due, HW4 out |
| 03/30 | Text mining: Attention Mechanism | |
| 13 | 04/04 | Graph mining: GNN | |
| 04/05 | Graph mining: GNN | |
| 04/06 | Graph mining: GNN | |
| 14 | 04/11 | Model interpretation | |
| 04/12 | Model interpretation | |
| 04/13 | Model robustness | |
| 15 | 04/18 | Model robustness | |
| 04/19 | Model fairness | HW4 due |
| 04/20 | Model fairness | |
| 16 | 04/25 | Project presentation | |
| 04/26 | Project presentation | |
| 04/27 | Project presentation | |
| 18 | 05/09 | Final Exam | |
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