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.
This course is anticipated to be oversubscribed, and as such registration is initially limited to CS students. If you have been unable to register, please see the CS department Course Access & Request Policy. Please do not ask the instructor for an override, we have been told that if the course is shown as full, the registrar will not allow registration even with a form 23 signed by the instructor, so you would just be wasting your time and ours. Please follow the process above or consult with your advisor.
Office hours to be held in HAAS 115 unless otherwise listed.
Unless your questions are very specific to material taught by one of the instructors, we recommend you talk to the instructor listed as primary for your section (9:30: Prof. ?, 10:30: Prof ?). This will help us get to know you better, which may be important when (for example) you are looking for recommendation letters.
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.
The course will primarily be taught through lectures, supplemented with reading.
The written assignments and projects are also a significant component of the learning experience. We will be using Gradescope to turn in and comment on assignments; Brightspace will be used for recording and distributing grades, as well as for any other non-public information about the course.
For review (and if you miss a lecture), you can pick them up as an Boilercast vodcast/podcast (accessible through Brightspace). 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 Ed Discussion or Piazza (still working on this) to facilitate discussions; this will enable you to post questions as well as respond to questions posted by others. Be aware that the default is for posts to be identified and visible to everyone. You are also encouraged to hold discussions with other students. Please keep the collaboration guidelines below in mind. Purdue has paid licenses for WebEx and Zoom, if you wish to meet remotely with other students.
We will be using 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 or be taking 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 on your understanding of the material as evidenced in:
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 not be accepted, except as follows. You are allowed five extension days, to be used at your discretion throughout the semester (illness, job interviews, etc.) You may use at most two days on each assignment (so that we can get solution sets out in a timely manner.) Fractional use is not allowed -- each late day is a 24 hour extension. Late submission will not be accepted more than 48 hours after the due date, or after you have used your late days.
Brightspace will be used to record/distribute grades, and for distributing some class-only materials.
Please read the departmental academic integrity policy. This will be followed unless we 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 we 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, we 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 us and/or note on what you turn in that
it represents collaborative effort (the same holds for information
obtained from other sources that provided substantial portions
of the solution.) If we feel you have gone beyond
acceptable limits, we 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 our 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 please feel free to talk to the instructors - if we can't help, we'll try to point you in the right direction. Be aware that due to Title IX and state law, there are some things for which we can't promise confidentiality (but see CARE below).
University Emergency Preparedness instructions. Note: In the event of weather-related class cancellation, we will probably transition to live
online (which would be recorded, as with Boilercast), which you should all be familiar with. If there is a weather-related cancellation, watch your email.
Nondiscrimination Statement: Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life. Purdue’s nondiscrimination policy can be found at http://www.purdue.edu/purdue/ea_eou_statement.html.
Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let us know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.
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 me (or, if you have an issue with me, to the department head.)
The texts below are recommended but not required. Reading materials will be distributed as necessary, through Brightspace Please check regularly.
The following are also useful:
You may also want to see the canonical syllabus.
Final Exam
Friday, 5 May, 13:00-15:00, WALC 1055 (Hiler Theater).
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 37300 exam, please let us know as soon as possible.
Note that conflicting exams are normally the only reason for rescheduling.
I bought a ticket to go home earlier
is not an accepted reason for
an exam to be rescheduled.