Child pages
  • Syllabus
Skip to end of metadata
Go to start of metadata

IST 557 Data Mining: Techniques and Applications

Course Information

Course Number: IST557

Credits: 3

Lecture Time: 3:05 PM - 4:20 PM Tuesday and Thursday

Location:   E205 Westgate Building

Instructor

Zhenhui (Jessie) Li

  • Office: E331 Westgate Building
  • Office Hour: 4:20-5:20 PM Tuesday, or by appointment
  • Email: JessieLi@ist.psu.edu

 

Teaching Assistant

Guanjie Zheng (for project starting from week 8)

  • Office: E342 Westgate building
  • Office Hour: 4:20-5:20 PM Thursday, or by appointment

  • Email: gjz5038@psu.edu

Hua Wei (for assignments)

  • Office: E342 Westgate building
  • Office Hour: 4:20-5:20 PM Thursday, or by appointment
  • Email: hzw77@ist.psu.edu

Course Objective

This course will introduce basic machine learning techniques and their applications to the real-world problems.

 

Course Prerequisite

Python, linear algebra, probability, algorithm analysis, data structure.

Note: This year we require all the students to know Python because assignments and projects will be implemented in Python.

 

Grading Policy

Assignment, quiz

45%

Project

45%

Class Attendance

10%

  • Assignments: All the assignments are done individually
  • Project: The course project is carried as a team of 2 students. 
  • Class attendance: Attending class is required. Excused absence should get approved by the instructor BEFORE the class. 

Late submission policy:

Submissions after the deadline but less than 24 hours late are accepted but penalized 10%, and submissions more than 24 hours but less than 48 hours late are penalized 30%. No submissions are accepted more than 48 hours late.

 

Final grading:

Score

Grade

93A
90A-
87B+
83B
80B-
77C+
70C
60D

This table indicates minimum guaranteed grades. Under certain limited circumstances (e.g., an unreasonably hard exam), we may select more generous ranges or scale the scores to adjust.

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. 

Kevin Patrick Murphy, Machine Learning: a Probabilistic Perspective, 2012 http://www.cs.ubc.ca/~murphyk/MLbook/

P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005

Academic Integrity

According to the Penn State Principles and University Code of Conduct: Academic integrity is a basic guiding principle for all academic activity at Penn State University, allowing the pursuit of scholarly activity in an open, honest, and responsible manner. In according with the University’s Code of Conduct, you must not engage in or tolerate academic dishonesty. This includes, but is not limited to cheating, plagiarism, fabrication of information or citations, facilitating acts of academic dishonesty by others, unauthorized possession of examinations, submitting work of another person, or work previously used without informing the instructor, or tampering with the academic work of other students. Any violation of academic integrity will be investigated, and where warranted, punitive action will be taken. For every incident when a penalty of any kind is assessed, a report must be filed.

*Plagiarism (Cheating): Talking over your ideas and getting comments on your writing from friends are NOT examples of plagiarism. Taking someone else's words (published or not) and calling them your own IS plagiarism. Plagiarism has dire consequences, including flunking the paper in question, flunking the course, and university disciplinary action, depending on the circumstances of the offense. The simplest way to avoid plagiarism is to document the sources of your information carefully.

  • No labels