Hua Wei

IS392 - Web Mining and Information Retrieval (Spring 2022)

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

Assignment, quiz	45% (5% quiz, 14% assignment 1, 13% assignment 2, 13% assignment 3)
Project	45% (10% report1, 10% report2, 10% report 3, 15% final report)
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.

IS 657 Spatiotemporal Urban Analytics (Fall 2022)

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

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.