CS 39000-DM0: Data Mining & Machine Learning

TR 15:00-16:15


Chris Clifton

Email: clifton_nospam@cs_nojunk.purdue.edu

Course Outline

Course Topics

This course will introduce students to the field of data mining and machine learning, which sits at the interface between statistics and computer science. Data mining and machine learning focuses on developing algorithms to automatically discover patterns and learn models of large datasets. This course introduces students to the process and main techniques in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation.

Teaching Assistants

Office Hours

Prof. Clifton's office hours Monday and Thursday 10-11 in LWSN 2142F. I am also available by appointment, email some good times and I'll pick what works. Or you can just drop by, I'm often in.

Mailing List

There will be a course email list used for high-priority announcements. This will use your @purdue.edu email address; make sure this is forwarded to someplace you look on a regular basis.

We will be using Blackboard for turning in assignments as well as recording and distributing grades, as well as any other non-public information about the course.

Course Methodology

The course will be taught through lectures, supplemented with reading. The written assignments and projects are also a significant component of the learning experience.

For review (and if you miss a lecture), you can pick them up as an Echo360 vodcast/podcast (accessible through Blackboard, or Echo360, log in via institution.) Be warned that the audio isn't great; you only see what is on the screen, not what is written on the chalkboard; and you can't ask (or answer) questions; so it isn't really a viable alternative to attending lecture.

We will be using Piazza to facilitate discussions; this will enable you to post questions as well as respond to questions posted by others. More information on accessing Piazza will be provided here soon.

We will be using iClickers or HotSeat for real-time feedback in class.


The formal prerequisite is CS 18200: Foundations Of Computer Science and CS 25100: Data Structures and Algorithms. You also must have either taken STAT 35000: Introduction to Statistics or STAT 51100: Statistical Methods. (If you have comparable courses, such as ECE 36800, please contact the instructor.)


Evaluation is a somewhat subjective process (see my grading standards), however it will be based primarily on your understanding of the material as evidenced in:

(Percentages above tentative.) Exams will be open note, with two 8.5x11 or A4 pages allowed (e.g., one piece of paper, double-sided). If any additional notes are allowed, these will be announced per exam. To avoid a disparity between resources available to different students, and the possibility of using communication-equipped devices in unethical ways, electronic aids are not permitted.

Late work will be penalized 15% per day (24 hour period or fraction thereof). You are allowed five extension days, to be used at your discretion throughout the semester (illness, job interviews, etc.) You must explicitly note that you are using these in the header of the assignment or it will be considered late (e.g., using extension days 2 and 3 for this assignment.) Fractional use is not allowed, and this may not be used to extend submission past the last day of class.

Blackboard will be used to record/distribute grades (and, in some cases, for turning in assignments.)

Policy on Intellectual Honesty

Please read the departmental academic integrity policy above. This will be followed unless I provide written documentation of exceptions. You should also be familiar with the Purdue University Code of Honor and Academic Integrity Guide for Students. You may also find Professor Spafford's course policy useful - while I do not apply it verbatim, it contains detail and some good examples that may help to clarify the policies above and those mentioned below.

In particular, I encourage interaction: you should feel free to discuss the course with other students. However, unless otherwise noted work turned in should reflect your own efforts and knowledge.

For example, if you are discussing an assignment with another student, and you feel you know the material better than the other student, think of yourself as a teacher. Your goal is to make sure that after your discussion, the student is capable of doing similar work independently; their turned-in assignment should reflect this capability. If you need to work through details, try to work on a related, but different, problem.

If you feel you may have overstepped these bounds, or are not sure, please come talk to me and/or note on what you turn in that it represents collaborative effort (the same holds for information obtained from other sources that you provided substantial portions of the solution.) If I feel you have gone beyond acceptable limits, I will let you know, and if necessary we will find an alternative way of ensuring you know the material. Help you receive in such a borderline case, if cited and not part of a pattern of egregious behavior, is not in my opinion academic dishonesty, and will at most result in a requirement that you demonstrate your knowledge in some alternate manner.

If you have other issues

University Emergency Preparedness instructions

Student Mental Health and Wellbeing: Purdue University is committed to advancing the mental health and wellbeing of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at (765)494-6995 and http://www.purdue.edu/caps/ during and after hours, on weekends and holidays, or through its counselors physically located in the Purdue University Student Health Center (PUSH) and the Psychology building (PSYC) during business hours.

Sexual Violence: Purdue University is devoted to fostering a secure, equitable, and inclusive community. If you or someone you know has been the victim of sexual violence and are interested in seeking help, there are services available. Reporting the incident to any Purdue faculty and certain other employees, including resident assistants, will lead to reference to the Title IX Coordinator, as these individuals are mandatory reporters. The Title IX office can investigate report of sex-based discrimination, sexual harassment, or sexual violence. Title IX ensures that both parties in a reported event have equal opportunity to be heard and participate in a grievance process. To file an online report visit https://cm.maxient.com/reportingform.php?PurdueUniv&layout_id=15 or contact the Title IX coordinator at 765-494-7255.

The Center for Advocacy, Response, and Education (CARE) offers confidential support and advocacy that does not require the filing of a report to the Title IX office. The CARE staff helps each survivor assess their reporting options and access resources that meet personal needs. The CARE office can be found at 205 North Russell Street in Duhme Hall (Windsor), room 143 Monday - Friday 8:00 AM to 5:00 PM. They can also be reached at their 24/7 hotline 765-495-CARE or at CARE@purdue.edu.

And you should always feel free to call, email, or drop by and talk to the instructor (or, if you have an issue with the instructor, the department head.)


The texts below are recommended but not required. Reading materials will be distributed as necessary, through blackboard. Please check regularly.

Syllabus (numbers correspond to roughly to week):

  1. Course Introduction
    Suggested reading:
  2. January 16: Martin Luther King Jr. Day
    Background and basics of Statistics
    Suggested reading: Notes in Blackboard.
  3. Guest lectures, Prof. Dan Goldwasser: Exploratory data analysis
    Suggested reading: Notes in Blackboard, Principles of Data Mining, Chapters 4.1-4.3, 2.
  4. Exploratory data analysis, Predictive Modeling
    Suggested reading: Notes in Blackboard, Principles of Data Mining, Chapters 3.1-3.6.
  5. Predictive Modeling
    Suggested reading: Notes in Blackboard, Principles of Data Mining, Chapters 5.1-5.3.1, 6.1-6.2.
  6. Predictive Modeling
    Suggested reading: Principles of Data Mining, Chapter 10-10.8.
  7. Predictive Modeling
    First Midterm: February 21, in class. (solutions.)
    Suggested reading: Principles of Data Mining, Chapter 8.
  8. Understanding and Extending Model Performance
    Suggested reading: Principles of Data Mining, Chapter 10.9-10.10.
  9. Descriptive Modeling
    March 10: Drop Date. March 13-18: Spring Break
    Suggested reading: Principles of Data Mining, Chapter 9.1, 9.3-9.5.
  10. Descriptive Modeling
    Suggested reading: Principles of Data Mining, Chapter 9.2, 9.6.
  11. Pattern Mining
    Suggested reading: Principles of Data Mining, Chapter 13-13.3.
  12. Anomaly Detection
  13. Data Mining Process
    Second Midterm: March 28 April 11, in class. (solutions.)
    Suggested reading: Principles of Data Mining, Chapter 13-13.3.
  14. Data Mining Process
    Suggested reading: CRISP-DM 1.0.
    April 18: Guest Lecture, Dr. Barry Nussbaum (talk video)
    Advanced Topics
  15. Advanced Topics, Review

Final Exam Monday, 1 May, 8:00am-10:00am, ME 1061.

If you have another exam scheduled at that time or you have three or more exams scheduled that day and would like to reschedule the 39000-DM0 exam, please let me know as soon as possible. Note that conflicting exams are pretty much the only reason for rescheduling, I bought a ticket to go home earlier is not an accepted reason for an exam to be rescheduled.

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