Mariam Salloum

Instructors

Mariam Salloum (prof)

• Email: msalloum [at] cs [dot] ucr [dot] edu

• Office: Bourns A (Room 159B)

• Office Hours: M 12:30 - 1:30, TH 1 - 2, and by appointment

TBD (TA)

• Email: TBD

• Office Hours: TBD

Announcements

• 9/25 Website online!!

Course Description

This course will cover models for information retrieval, techniques for indexing and searching, and algorithms for classification and clustering. It will also cover latent semantic indexing, link analysis and ranking, Map-Reduce architecture and Hadoop, to different degrees of detail, time permitting.

• Efficient text indexing

• Boolean and vector-space retrieval models

• Evaluation and interface issues

• IR techniques for the web, including crawling, link-based algorithms, and metadata usage

• Document clustering and classification

• Traditional and machine learning-based ranking approaches

• Social networks search

Course Logistics

iLearn

• Will be used to post grades

Google Drive

• Lectures slides will be placed on a shared Google Drive: https://drive.google.com/drive/folders/11yRclP5N4M5AyfLRIfbnpDxtv9fmu8kF?usp=sharing

Campus Wire

• CampusWire will be used for discussions- announcements. Questions relating to lecture or assignment should be posted to discussion board, not emailed to teachers, so any teacher/student can respond and fellow students benefit from answers.

• LINK: https://campuswire.com/c/G16C76071

• CODE: 3522

Textbooks

• Search Engines: Information Retrieval in Practice
  Bruce Croft, Donald Metzler, Trevor Strohman
  Addison Wesley; 1 edition (February 16, 2009)
  ISBN-10: 0136072240/ISBN-13: 978-0136072249
  Available here : http://ciir.cs.umass.edu/downloads/SEIRiP.pdf
  

• Mining of Massive Datasets
  Jure Leskovec, Anand Rajaraman, Jeff Ullman
  Available here : http://www.mmds.org/
  

Also recommended for reference:

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press. 2008.

• Modern Information Retrieval the concepts and technology behind search

• Hearst, M.A. Search User Interfaces, Cambridge University Press, September, 2009

Grade Breakdown

Grades will be weighted as follows:

• Assignments: This course is designed to be a hands-on learning experience. I believe that students learn better by doing. As part of this philosophy, there will be a series of homework assignment sduring the quarter. These assignments are meant to help you learn the theory covered in class by practical implementation of the concepts.

• • Late Homework Policy: All of the assignments are to be submitted electronically. Always check the assignment page for due dates. Assignments can be submitted up to amaximum of 3 days past the deadline. There will be a deduction of 10% penalty for each day late.

• Midterms: There will be two written in-class midterms during the quarter. Both midterms are closed book/notes. There will no makeup exams unless you let me know of any conflicts ahead of time and bring a doctor’s note. We will have an in-class review the Tuesday before the midterm. I will usually handout a study guide the week before the midterm.

• • Midterm 1 on Thursday Oct. 24th

  • Midterm 2 on Thursday Nov. 21st

• Project: There will be a team-based project toward the end of the quarter (instead of a final exam). You may work in teams of three to build a information retrieval system. More details will be provided later in the quarter.Important Note: I expect that in most cases, everyone in the group will get the same grade. However I reserve the right to give different grades to students in the same group if I feel that it is warranted. This will be based on contribution outlined in status reports and the team assessment.

• Quizzes: We will have several short in-class quizzes. Quizzes will be announced in the previous class. The objective of the quizzes is to allow you to review for the midterm. Makeup quizzes will not be allowed given that the quizzes only account for a small portion of the grade and missing one quiz will not affect your grade.

• Class Participation: Attendance to class and lab discussion is expected. Active participation during lecture along with completion of in-class exercises and surveys will determine the participation grade.

Academic Integrity

Academic integrity is fundamentally about ethical behavior. Appropriate collaboration and research of previous work is an important part of the learning process. However, not all collaboration or use of existing work is ethical. The overarching principles which should guide you when determining whether or not it is appropriate to use a source or collaborate with a classmate involve answering these questions: Does this fit within the spirit of the assignment/activity?

In any ethical decision there is always judgment involved. Some assignments and activities involve collaborating with a team, in others you are asked to work individually. You are expected to have some common sense and to use it.

Does this help me or someone else in the class to improve our skills and/or understanding of class material?

As a guiding principle, talking about concepts is usually good, talking about specific answers or approaches to problems is usually not.

Does this misrepresent my own (or someone else's) capabilities and understanding of materials for the purpose of grading?

Attribution of sources is a key idea here; if you use work which is not your own, that work should be cited. For this class, citation is not required to be in a specific format, but any citation should clearly identify the author and source of any work which is not your own. Refer to the university policy on plagiarism and cheating.

Have any specific instructions been given for this assignment?

Not all assignments are the same. On some you will be given explicit instructions about what level of collaboration is appropriate, and you are expected to abide by those restrictions even if you disagree with them.

If you are at all uncertain about an action, whether it be working with another student, researching existing code, or something else, you are always welcome to ask the instructor for clarification.

The severity of sanctions imposed for an academic integrity violation will depend on the severity of the transgression and ascertained intent of the student. Penalties may range from failing the assignment to failing the course. Again, actions will adhere to the Academic Honesty policies of BCOE and UCR.