Course Information:
|
Instructor: Chengkai Li
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TA: Saravanan Thirumuruganathan
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Course
Description: This is an introductory course on data mining. Data
Mining refers to the process of automatic discovery of patterns and knowledge
from large data repositories, including databases, data warehouses, Web,
document collections, and data streams. We will study the basic
topics of data mining, including data preprocessing, data warehousing and OLAP,
data cube, frequent pattern and association rule mining, correlation analysis,
classification and prediction, and clustering, as well as advanced topics
covering the techniques and applications of data mining in Web and text.
Prerequisites:
CSE 3330/5330 Database Systems
I
or
CSE
4331/5331 Database Systems
II or similar
courses or consent of instructor
Announcements: Stay tuned and make sure to check Blackboard frequently. Important announcements will be posted there.
Regrading: Regrading request must be made within 7 days after we post scores on Blackboard. TA will handle regrade requests. If student is not satisfied with the regarding results, you get 7 days to request again. The instructor will regrade, and the decision is final.
Ethics Policies and Academic Integrity: The College cannot and will not tolerate any form of academic dishonesty by its students. This includes, but is not limited to cheating on examinations, plagiarism, or collusion (explained in the document below). Students are required to read the following document carefully, sign it, return the signed copy to the instructor, and keep a copy for their own records. Hardcopies of this document will be provided to the students in the first class, and also can be picked up in the instructor's office. If you print by yourself, please make it double-sided.
Statement on Ethics, Professionalism, and Conduct for Engineering Students
Miscellaneous: If you require accommodation based on disability, I would like to meet with you in the privacy of my office during the first week of the semester to ensure that you are appropriately accommodated. Please read the page of the office for students with disabilities.
Schedule:
Date | # |
Lecture |
Assignment |
Lecture Notes |
Extra Reading | |
Out |
Due |
|||||
08/25 | 1 | Course Overview | [PPT] | |||
08/30 | 2 |
Introduction
(Chapter 1) |
[PPT] | |||
09/01 | 3 | Prominent Streak Discovery | HW1 | [PPT] | Prominent Streak paper | |
Data Warehousing, OLAP, Data Cube (Chapter 3, 4) |
||||||
09/06 | 4 | Prominent Streak Discovery | ||||
09/08 | 5 | Course Project | ||||
09/13 | 6 | Data Warehousing, OLAP, Data Cube | [PPT] | |||
09/15 | 7 | Data Warehousing, OLAP, Data Cube |
HW1 |
|||
09/20 | 8 | Data Warehousing, OLAP, Data Cube | ||||
Classification and Prediction (Chapter 6) | ||||||
09/22 | 9 | Decision Tree | [PPT] | |||
09/27 | 10 |
Decision Tree |
||||
09/29 | 11 | Bayesian Classifiers | HW2 | [PPT] | ||
10/04 | 12 | Bayesian Classifiers (cont'd) | ||||
10/06 | 13 |
|
[PPT] | |||
10/11 | 14 |
|
HW2 Project Proposal |
[PPT] | ||
10/13 | Midterm Exam (Thursday, Oct. 13th, 9:30am-10:50am, ERB130) | |||||
10/18 | 15 | Evaluating Classification Models | [PPT] | |||
10/20 | 16 |
Evaluating Classification
Models |
HW3 | |||
10/25 | 17 | Support Vector Machine | [PPT] | |||
Clustering
(Chapter 7) |
||||||
10/27 | 18 | Overview of Clustering, Similarity/Dissimilarity Measure | [PPT] | |||
Text and Web Mining (1) | ||||||
11/01 | 19 | Vector Space Model | [PDF] | textbook excerpt | ||
11/03 | 20 |
|
document camera | |||
Clustering
(Chapter 7) |
||||||
11/08 | 21 |
K-means |
Project Progress |
[PPT] document camera |
||
11/10 | 22 |
K-means |
Project Progress | |||
11/15 | 23 |
Hierarchical |
[PPT] | |||
11/17 | 24 |
Hierarchical |
textbook excerpt (in Blackboard) | |||
Frequent Pattern and Association Rule Mining (Chapter 5) |
||||||
11/22 | 25 | Association Rule Mining | HW3 | [PPT] | ||
11/24 | Thanksgiving Holidays | |||||
11/29 | 26 | Correlation Analysis | [PPT] | |||
Text and Web Mining (2) | ||||||
12/01 | 27 |
Link Analysis: PageRank |
[PDF] | textbook excerpt | ||
12/06 | 28 | MapReduce | [PPT] | |||
12/08 | 29 | Final Review | Project Report | [PPT] | ||
12/09 | Project Demo/Presentation (Friday, Dec. 9th, ERB 501) | |||||
12/15 |
Final Exam
(Thursday, Dec. 15th,
8am-10:30am, ERB 130) |