INLS 509:
Information Retrieval

Description: The field of information retrieval (IR) is concerned with the analysis, organization, storage, and retrieval of unstructured and semi-structured data. In this course, we will focus on mostly text. While IR systems are often associated with Web search engines (e.g., Google), IR applications also include digital library search, patent search, search for local businesses, and expert search, to name a few. Likewise, IR techniques (the underlying technology behind IR systems) are used to solve a wide range of problems, such as organizing documents into an ontology, recommending news stories to users, detecting spam, and predicting reading difficulty. This course will provide an overview of the theory, implementation, and evaluation of IR systems and IR techniques. In particular, we will explore how search engines work, how they "interpret" human language, what different users expect from them, how they are evaluated, why they sometimes fail, and how they might be improved in the future.
Prerequisites: There are no prerequisites for this course.
Expectations: Information retrieval is the study of computer-based solutions to a human problem. Thus, the first half of the course will be system-focused, while the second half will be user-focused. During the first half, you should expect to see some math (e.g., basic probability and statistics and some linear algebra). However, we will focus on the concepts rather than the details.

Students will have an opportunity to explore their interests with a open-ended literature review.

Time & Location: M 5:45-8:30pm, Manning 001
Instructor: Jaime Arguello (email, web)
Office Hours: By appointment, Manning 10 (Garden Level)
Required Textbook: Search Engines - Information Retrieval in Practice, W. B. Croft, D. Metzler, and T. Strohman. Cambridge University Press. 2009. Available on-line.
Additional Resources: Foundations of Statistical Natural Language Processing. C. Manning and H Schutze. 1999.

Introduction to Information Retrieval. C. Manning, P. Raghavan and H. Schutze. 2008.
Other Readings: Selected papers and chapters from other books will sometimes be assigned for reading. These will be available online.
Course Policies: Laptops, Attendance, Participation, Collaboration, Plagiarism & Cheating, Late Policy
Grading: 30% homework (10% each)
15% midterm exam
15% final exam
30% literature review (5% proposal, 10% presentation, 15% paper)
10% participation
Grade Assignments: Undergraduate grading scale: A+ 97-100%, A 94-96%, A- 90-93%, B+ 87-89%, B 84-86, B- 80-83%, C+ 77-79%, C 74-76%, C- 70-73%, D+ 67-69%, D 64-66%, D- 60-63%, F 0-59%

Graduate grading scale: H 95-100%, P 80-94%, L 60-79%, and F 0-59%.

All assignments, exams, and the literature review will be graded on a curve.
Schedule: Subject to change! The required textbook (Croft, Metzler, and Strohman) is denoted as CMS below.
Lecture Date Events Topic Reading Due
1 Mon. 1/13   Introduction to IR: The Big Picture, Course Overview CMS Ch. 1
2 Mon. 1/20 MLK Day (No class)    
3 Mon. 1/27 HW1 Out Introduction To Ad-hoc Retrieval, Indexing and Query Processing CMS Ch. 2, 5.3.0-5.3.3, 7.1.0-7.1.1
4 Mon. 2/3   Statistical Properties of Text CMS Ch. 4.1-4.2
5 Mon. 2/10 HW1 Due Text Representation CMS Ch. 4.3-4.7, MRS Ch. 2
6 Mon. 2/17 Literature Review Proposal Due, HW2 Out Retrieval Models: Vector Space Model CMS Ch. 7.0-7.1.2
7 Mon. 2/24   Retrieval Models: Query Likelihood Model CMS Ch. 7.3, CMS 4.5
8 Mon. 3/2 HW2 Due Document Priors,Evaluation Overview CMS Ch. 8
9 Mon. 3/9 Spring Break (No class)    
10Mon. 3/16Spring Break II (No class)  
10Mon. 3/23 Test Collection Evaluation, Midterm ReviewRobertson '08, Sanderson '10 (page 248-298), Hersh et al., '00, Turpin & Hersh '01
11Wed. 3/25MidtermMidterm 
12Mon. 3/30HW3 OutEvaluation MetricsSanderson '10 (page 308-350)
13Mon. 4/6 ExperimentationSmucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
14Mon. 4/13HW3 DueA/B Testing (Optional)Dmitriev et al., '17, Video Tutorial (Kohavi et al. '17)
15Mon. 4/20 Student Presentations 
16Fri. 4/24Literature Review Due  
17Thu. 4/30Final ExamFinal Exam