CSE 572 - Data Mining (Fall 2023/Spring 2024 @ASU)
Course Number: CSE 572
Faculty Instructor: Hua Wei, Ph.D.
E-mail: hua.wei AT asu.edu
Overview
This course will introduce fundamental concepts and techniques in data mining including classification, clustering, dimensionality reduction, and outlier detection. Students will learn the theory behind topics as well as gain hands-on experience implementing data mining techniques and applying them to real world problems and data. Students will learn how data mining is used in research and gain understanding and practice of the complete research process.
Prerequisites
- Students are expected to have a working knowledge of basic probability theory, linear algebra, and the academic research process.
- Note: Assignments and projects should be implemented in Python.
Textbook
There is no required textbook. Below are some recommended reference books.
- Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed.
- Chris Bishop, Pattern Recognition and Machine Learning.
- Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar, Introduction to Data Mining, 2nd ed.
Assignments and Grading
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Participation
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5%
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|
In class lab assignment
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20%
|
|
Assignment, quiz
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35% (5% quiz, 10% assignment 1, 10% assignment 2, 10% assignment 3)
|
|
Project
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40% (5% proposal, 5% literature review, 5% progress report, 15% final presentation, 10% final report)
|
- Assignments: All the assignments are done individually.
- Project: The course project is carried as a team.
- Class attendance: Attending class is required. Excused absence should get approved by the instructor BEFORE the class.
IS392 - Web Mining and Information Retrieval (Spring 2022/2023 @NJIT)
Course Number: IS392-002
Classroom: Tiernan Hall 113 (after Jan. 30)
Class Meets: 11:30 am - 12:50 pm, Monday & Wednesday,
Faculty Instructor: Hua Wei, Ph.D.
E-mail: hua.wei AT njit.edu
Office: GITC 3803H
Office Hours: Monday 1-2 pm, or by appointment
Overview
This course introduces the design, implementation, and evaluation of web mining applications. Topics include automatic indexing, natural language processing, retrieval algorithms, basic machine learning techniques, and their applications to the web data. Students will gain hands-on experience applying theories in case studies.
Prerequisites
- IS218 OR IT114 OR CS114
- Programming, linear algebra, probability, algorithm analysis, data structure.
- Note: Assignments and projects should be implemented in Python.
Textbook
There is no required textbook. Below are some recommended reference books.
- Search Engines: Information Retrieval in Practice, by Croft, Metzler, and Strohman. Publisher: Addision-Wesley
- Paper 1: What Do People from Information Retrieval?, W. Bruce Croft
- Paper 2: Search Engine Optimization Starter Guide, Google
- Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed.
- Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, 2012./li>
Assignments and Grading
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Assignment, quiz
|
45% (5% quiz, 14% assignment 1, 13% assignment 2, 13% assignment 3)
|
|
Project
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45% (10% report1, 10% report2, 10% report 3, 15% final report)
|
|
Class Attendance
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10%
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- Assignments: All the assignments are done individually.
- Project: The course project is carried as a team.
- Class attendance: Attending class is required. Excused absence should get approved by the instructor BEFORE the class.
IS 657 Spatiotemporal Urban Analytics (Fall 2022 @NJIT)
Course Number: IS657
Classroom: Jersey City 101
Class Meets: Tuesday from 6:00 - 8:50 pm,
Faculty Instructor: Hua Wei, Ph.D.
E-mail: hua.wei AT njit.edu
Office: Faculty office @JerseyCity
Office Hours: Tuesday 4 - 6 pm, or by appointment
Overview
Cities now generate an immense amount of publicly accessible data that allows us to ask and answer new questions about cities and urban populations. This course will teach the methods, models, and tools for data-driven urban research. You will learn the basics of urban data acquisition, ethics, management, visualization, and statistical analysis with a focus on spatio-temporal data. By the end of the course, you will be able to formulate a question relevant to urban science and then acquire, prepare, and analyze data to gain insights and aid in decision making. The class format will include lectures from the professor, labs using Python, readings, discussion, and student projects.
Prerequisites
- IS-665
- Basic Python programming knowledge (Required since language instruction will not be covered in class)
Textbook
There is no required textbook. Below are some recommended reference books.
- Singleton, Spielman, and Folch. “Urban Analytics”
- Zheng. “Urban Computing”
- Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed.
Assignments and Grading
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Assignment, quiz
|
40%
|
|
Project
|
50%
|
|
Class Attendance
|
10%
|
- Assignments: All the assignments are done individually.
- Project: The course project is carried as a team.
- Class attendance: Attending class is required. Excused absence should get approved by the instructor BEFORE the class.
