CS 583 Spring 2015

CS 583 - Spring 2017 (two sections)

Data Mining and Text Mining

NOTE: Roksana M Sady (sady2@uic.edu) maintains a queue for those who could not sign up for the course. Please contact her to add you to the queue. She will contact you when there is a slot for you.

Course Objective

This course has three objectives. First, to provide students with a sound basis in data mining tasks and techniques. Second, to ensure that students are able to read, and critically evaluate data mining research papers. Third, to ensue that students are able to implement and to use some of the important data mining and text mining algorithms.

Think and Ask!

If you have questions about any topic or assignment, DO ASK me or even your classmates for help, I am here to make the course undersdood. DO NOT delay your questions. There is no such thing as a stupid question. The only obstacle to learning is laziness.

General Information

  • Instructor: Bing Liu
    • Email: Bing Liu
    • Tel: (312) 355 1318
    • Office: SEO 931
  • Teaching Assistant: Geli Fei
    • Email: gfei2@uic.edu
    • Office hours: by appointment

Section 1

  • Course Call Number: 25479
  • Lecture time slot:
    • 12:30-1:45pm Tuesday & Thursday
  • Lecture hall: LC A3
  • Office hours: 2:00pm-3:15pm, Tuesday & Thursday (or by appointment)

Section 2

  • Course Call Number: 39840
  • Lecture time slot:
    • 3:30-4:45pm Tuesday & Thursday
  • Lecture hall: LC A2
  • Office hours: 2:00pm-3:15pm, Tuesday & Thursday (or by appointment)

Grading

Prerequisites

Teaching materials

Topics (subject to change; the reading list follows each chapter title)

  1. Introduction
  2. Data pre-processing
  3. Association rules and sequential patterns (Sections 2.1 - 2.7)
  4. Supervised learning (Classification) (Chapter 3)
  5. Unsupervised learning (Clustering) (Chapter 4)
  6. Information retrieval and Web search (Sections 6.1 - 6.6, and 6.8)
  7. Semi-supervised learning (Sections 5.1.1, 5.1.2, 5.2.1 - 5.2.4)
  8. Lifelong machine learning (chapter 1; sections 2.1, 2.2, 3.1, 3.2, 3.4, 4.4, 4.5; chapter 5)
  9. Social network analysis (Sections 7.1 - 7.4)
  10. Sentiment analysis and opinion mining (Sections 11.1 - 11.6; check out my two books)
  11. Recommender systems and collaborative filtering (Section 12.4)
  12. Web data extraction (Sections 9.1 and 9.2)

Projects - graded (you will demo your programs to me)


Rules and Policies


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By Bing Liu, Jan 8, 2017