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,W 11:15am-12:30pm, Manning 208 (in-person)
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, Use of Generative AI Tools
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
1Mon. 8/21 Introduction to Information Retrieval: The Big Picture 
2Wed. 8/23 Course Overview: Roadmap and ExpectationsCMS Ch. 1
3Mon. 8/28 Introduction To Ad-hoc Retrieval ICMS Ch. 2, CMS 7.0-7.1
4Wed. 8/30Class Cancelled  
5Mon. 9/4Labor Day (No Class)  
6Wed. 9/6HW1 OutIntroduction To Ad-hoc Retrieval II 
7Mon. 9/11 Statistical Properties of Text ICMS Ch. 4.0-4.2
8Wed. 9/13 Statistical Properties of Text II 
9Mon. 9/18 Text RepresentationCMS Ch. 4.3-4.7, MRS Ch. 2
10Wed. 9/20HW1 DueVector Space Model ICMS Ch. 7.0-7.1.2
11Mon. 9/25Well-Being Day (No Class)  
12Wed. 9/27HW2 OutVector Space Model II 
13Mon. 10/2 Query Likelihood Model ICMS Ch. 7.3, CMS 4.5
14Wed. 10/4Literature Review Proposal DueQuery Likelihood Model I 
15Mon. 10/9 Pseudo-relevance Feedback 
16Wed. 10/11HW2 DueEvaluation OverviewCMS Ch. 8
21Mon. 10/16Midterm ReviewMidterm Review 
22Wed. 10/18MidtermMidterm 
23Mon. 10/23 Test Collection Evaluation ISanderson '10 (pages 248-298), Hersh et al., '00, Turpin & Hersh '01
24Wed. 10/25 Test Collection Evaluation IISanderson '10 (pages 308-350)
25Mon. 10/30HW3 OutExperimentaion ISmucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
26Wed. 11/1 Experimentaion II 
27Mon. 11/6 Interactive Information Retrieval I 
28Wed. 11/8 Interactive Information Retrieval II 
29Mon. 11/13HW3 DueSearch Log AnalysisJoachims et al., '05
30Wed. 11/15TREC Conference (No Class)  
31Mon. 11/20 A/B Testinig IDmitriev et al., '17, Video Tutorial (Kohavi et al. '17)
32Wed. 11/22Thanksgiving (No Class)  
33Mon. 11/27 A/B Testing II 
34Wed. 11/29 Literature Review Presentations I 
35Mon. 12/4 Literature Review Presentations II 
36Wed. 12/6 Literature Review Presentations III 
37TBDLiterature Review Due  
38TBDFinal Exam