This course teaches important concepts and knowledge of information retrieval for managing unstructured data such as text data on Web or in emails. At the same time, students will be exposed to a large number of important applications. Students in the course will get hands on experience from homework and a course project. The first part of the course focuses on general concepts/techniques such as stemming, indexing, vector space model, and feedback procedure. The second part of the course shows how to apply the set of techniques on different applications such as Web search, text categorization, and information recommendation.
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 follow the CS department process for waitlisting/registration. Please do not ask the instructor for an override, I 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. Please follow the process above or consult with your advisor.
Office hour times, locations, and procedures to be determined and may change through the semester, as we learn more of social distancing requirements and facility availability. Please watch this space for changes, and check Piazza for potential temporary adjustments. TAs will also be available remotely during office hours at the posted links.
Thursday 8:30-10, WebEx 120 210 9752 password InfoRetrieval
In person meetings in LWSN 2116E by appointment, to avoid crowding. Email a few good times for you and I'll pick what works. (This also works for setting up a videoconference, if Thursday morning doesn't work.) Monday, Wednesday, and Friday mornings are currently the most open, but I have quite a few free slots throughout the week. University policy permitting, you can just drop by, I'm often in, and if not tied up with something that has be be finished right away I'll be happy to meet with you.
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 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.
The course will primarily be taught through lectures, supplemented with
reading.
The lecture delivery method is subject to change based on COVID-19 related
restrictions, but there will always be some form of online access to lecture
material.
Initial plans are for live
in-class lectures, recorded and made
available through
Boilercast.
We are currently in a room that supports real-time access to Boilercast, given
student demand I will try to set up live interactive access to the lecture
(e.g., WebEx or Zoom.)
The written assignments and projects are also a significant
component of the learning experience.
We will be using Piazza or Brightspace 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.
We may be using some form of real-time feedback in class. (Note that my standard practice is to drop approximately the lowest 10-15% of in-class response scores to allow for absences.)
The formal prerequisite is CS 25100: Data Structures and Algorithms (or ECE 36800). It will help if you have taken CS37300: Data Mining and Machine Learning and/or a statistics course such as 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:
All assigned work (including exams) will be provided and submitted online. Exact formats will be evolving, you can get some idea from looking at how midterm 2 and the final were handled in CS37300
Late work will be penalized 15% per day (24 hour period or fraction thereof).
The penalty is based on possible points, not your actual score
(so after 5 days, if your submission garners less than 75% of the possible points,
you get a 0.) Each assignment has a hard deadline of five days after the published due date,
after which the solution sets go out and no further submissions are accepted.
You are allowed five seven extension days, to be used at your
discretion throughout the semester (illness, job interviews, etc.); no penalty is assessed for late work within this limit.
If your assignments add up to more than five days late over the semester, the late days will be automatically applied to the highest value assignments (e.g., projects), so the 15% late penalties are applied to the lower value assignments first.
Late penalties will only be applied at the end of the semester, so if you go beyond the alotted late days, you may see your score for some assignments drop. You must keep track of late days yourself.
Fractional use is not allowed, and this may not be used to
extend submission past the hard deadline.
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 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 please feel free to talk to me - if I can't help, I'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 I can't promise confidentiality (but see CARE below).
University Emergency Preparedness instructions
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 me 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 basic text for this course is:
The following book may also be of interest, as it gives a somewhat different treatment of the material. You don't need both books, this should be considered optional reading.
A re-examination of text categorization methods, SIGIR'99
Right to be forgotten
Final Exam
Monday, December 7, 9:00amEST (14:00UTC) - Tuesday, December 8, 9:00amEST.
A two hour comprehensive exam, same format and delivery mechanism as the midterms.
If you have
three or more exams scheduled that day and would like to reschedule
this exam, please let me know as soon as possible. Note that
conflicting exams are pretty much the only reason for rescheduling.
For those leaving campus before final exams, please check to make sure you have reasonable connectivity to Gradescope before the exam. If not, please email the instructor (or call at 765-494-6005) to explain the situation so we can make alternate arrangements.