An introduction to the theories and techniques used to construct search engines. Topics include search interfaces, traditional retrieval models (e.g., TF-IDF, BM25), modern retrieval techniques (e.g., neural reranking and retrieval), search engine evaluation, and search applications (e.g., conversational IR, enterprise search). Students will also review current IR research topics, and complete a group project in which they will design and execute experiments for search engine components.
Credit Hours: 3
Prerequisites
Undergraduate (CSCI 536): CSCI-331 or equivalent courses. Students may not take this course and CSCI-636.
Graduate (CSCI 636): Completion of MS bridge courses or equivalent (e.g., PhD student status), including familiarity with basic computer science concepts. Students who have taken CSCI-536 may not also take this course.
Lectures (Spring 2025): Tuesday and Thursday 2:00pm - 3:15pm (Louise Slaughter Hall, Room 2150). Lectures will be both in-person and over Zoom. Lectures will be recorded and available through MyCourses.
10 quizzes will be given out weekly in the course.
The two lowest quiz grades will be dropped.
4 individual assignments will completed (11.25% each).
Assignments include written and programming questions.
3 research paper summaries (5% each).
The first paper will be discussed around the end of the first month of classes.
(Group Project) Draft of final project report (6 pages) along with rough work.
Project proposals and final projects will be completed in groups of 3 students.
(Group Project) Final report (8-10 pages), presentation during exam, rough work + project code.
The final project presentations will be given during the course exam slot (5 minutes, plus 2 minutes for questions).
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|
Grade Range (%) |
Final Letter Grade |
|---|---|
|
90-92 / 93+ |
A- / A |
|
80-82 / 83-86 / 87-89 |
B- / B / B+ |
|
70-72 / 73-76 / 77-79 |
C- / C / C+ |
|
60-69 |
D |
|
< 60 |
F |
For full points, all deliverables in the course including question answers, code, presentations and write-ups must be:
(1) Correct and complete (i.e., all parts of a question are answered with no errors and no omissions),
(2) Justified when an explanation is requested,
(3) Clear (i.e., understandable with a reasonable effort), and
(4) In the requested format, including both the forms of answers (e.g., not providing bullets when prose is asked for), and file types (e.g., providing a PDF as asked, versus providing a Word file). All graded work must be submitted as instructed, and will receive a small grade penalty otherwise.
**UPDATE: Revision policy for Spring 2025** For (1) research paper summaries, and (2) project proposals, students/groups will be permitted to resubmit a corrected version of their submission up to 7 days after the graded write-ups are returned (unless noted otherwise).
In a revised submission, corrections in the write-up may be indicated using (1) Word "TrackChanges", (2) an annotated PDF, or (3) by including an additional brief, 1-2 paragraph summary of changes on a separate page along with the updated document. Any other additional materials (e.g., spreadsheets) should provided in a new file, so that comparison to the original submission can be easily made. Please Note: Late penalties for an original submission will still be applied to a revised submission.
*Unless instructed otherwise*, writing and code for assignments and projects may not be produced using generative AI tools like ChatGPT, Llama, Bard, etc. This is because when learning fundamental skills, you need to ensure that you master the basics.
Please save your initial outline and drafts as evidence that you created the work from scratch in case there is any doubt about authorship or plagiarism. Coding solutions must be your own work, which means you cannot use generative AI tools in any manner to write your programs.
If authorship is in doubt, I may ask you to explain your answers/code, or to re-create aspects of your written answers and/or programs in a one-on-one meeting, to confirm that you have mastered the fundamentals needed to produce your answer or program.
Late Quizzes: Late MyCourse quiz completions will receive a grade of 0 unless permission to submit late has been granted by the instructor before the quiz deadline; because the two lowest quiz grades are dropped, extensions for quizzes are rare.
Late Assignment, Paper Summaries & Proposals (During Term, Before Exams): Submissions may be up to 1 week later, but with a 10% penalty after the deadline. Note that late submissions may receive feedback later than on-time submissions.
Final Project Presentation and Deliverables: No late submissions will be accepted for the final project.
1. Students will describe the basics of search interface and search engine architecture and design. (Assignments, Quizzes)
2. Students will select appropriate techniques to address information retrieval problems. (Assignments and Projects)
3. Students will apply performance evaluation methods for information retrieval. (Assignments, Quizzes, and Projects)
4. Students will apply information retrieval techniques. (Assignments and Projects)
5. Students will summarize modern information retrieval techniques verbally and in writing. (Projects and Research Paper Summaries)
Contact information for the instructor is available through the Contact page.
Office Hours: Office hours will be held in-person in Prof. Zanibbi's office (GOL 3551) and over zoom. Office hours will not be recorded. Guidance for assignments in office hours will be provided after assignments have been reviewed, and required readings/tasks for the assignment have been completed. Students will be instructed to return with questions after consulting pertinent materials otherwise.
To fully benefit from this course on searching information, students need to work with provided information before seeking clarification. Clarification of concepts from lecture, readings, code, etc. that students have reviewed (toward an assignment or otherwise), and questions about study strategies, professional development, etc. are always welcome.
Regarding Participation in Group Projects: A core part of this course is completing a group project, in which an experiment with a research IR system is proposed, executed, and then reported. The normal/preferred path is that all group members collaborate on the project as a team, and receive a single grade, on the expectation that team members contribute equally.
The project proposal will include an assignment of responsibilities to group members. If while creating the proposal or afterward you have concerns about the contributions of team members in your group, email the instructor. In cases where discussion cannot resolve concerns, we will arrange a group meeting and review the breakdown of responsibilities between team members, insure that they are fair to group members, and where necessary, discuss grade adjustments.
Emails and Class Discord (online chat): Use the course discord channel to ask clarifying questions about assignments and course material, and for general discussion about the course. You should not ask for answers to questions, or ask others to check your answers. Do not use the discord channel for anything other than discussions about the course with your instructor, TA, and classmates. You are always welcome to email the instructor if you prefer.
Instructor responses to discord/email: The instructor will try to respond to emails and discord messages within 24 hours during the workweek (i.e., Mondays at 9am through Friday at 5pm). Emails/messages received between 5pm on Thursday and 9am Monday will be replied to by Tuesday morning.
Note that emails received the day of a deadline may not be responded to until the following day.
RIT Attendance Policy. Attendance requirements are described in RIT Policy D0.4.0 Attendance. Some key details:
- Students are expected to attend all classes on-time.
- Students must make arrangements in advance of absences in order to fulfill course requirements.
- Students do not need to file excuses for absences.
- Instructors are not required to maintain attendance records, but must report prolonged absences to the student's advisor or department.
Illness. In the event of illness, students should continue to notify faculty directly that they will need to be absent and when they anticipate being able to rejoin the class. Per Policy D04.0 – Attendance, students are still responsible for fulfilling normal course requirements during their absence. Students are not required to provide details about or documentation related to health-related absences.
Rescheduling an Exam. Final projects completed during exam week cannot be made up except for real emergencies in which case proper documentation (like a doctor's note) will be required. Please see RIT's Academic Senate Final Examination Policy for related questions.
If you require accommodations, please let the instructor know so that we can be of assistance.
RIT ADA Statement (from MyCourses). The Disability Services Office is dedicated to facilitating equitable access to the full RIT experience for students with disabilities. We value disability as diversity and work in collaboration with campus partners to foster a welcoming, diverse, and inclusive campus community.
Any RIT student with a permanent or temporary disability can register and request accommodations with the Disability Services Office. Accommodations are determined on a case-by-case basis via a student-centered process, taking into account what is most appropriate and reasonable for an individual student. Visit www.rit.edu/dso to learn more.