This schedule is subject to changes as the course progresses.

Lecture slides are available in the "Files" section of CourseWorks.

• Jan 16: Course Overview. Information Retrieval I.
  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, 2008. Chapter 1 ("Boolean Retrieval")
• Jan 23: Information Retrieval II.
  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, 2008. Chapter 6 ("Scoring, Term Weighting, and the Vector Space Model")
  • Amit Singhal, Modern Information Retrieval: A Brief Overview, IEEE Data Engineering Bulletin, 2001
• Jan 30, Feb 6: Information Retrieval III.
  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, 2008. Chapter 11 ("Probabilistic Information Retrieval") and Chapter 7 ("Computing Scores in a Complete Search System")
• Feb 13: Web Search I.
  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schuetze, Introduction to Information Retrieval, 2008. Chapter 19 ("Web Search Basics") and Chapter 21 ("Link Analysis")
  • Sergey Brin and Larry Page: The Anatomy of a Large-Scale Hypertextual Web Search Engine, WWW7, 1998
• Feb 20: Information Extraction.
  • Eugene Agichtein and Luis Gravano, Snowball: Extracting Relations from Large Plain-Text Collections, in ACM DL 2000
  • Alpa Jain, Panagiotis Ipeirotis and Luis Gravano, Building Query Optimizers for Information Extraction: The SQoUT Project, in SIGMOD Record, Special Issue on "Managing Information Extraction," vol. 37, no. 4, December 2008
• Feb 27: Web Search II.
  • Bhaskar Mitra and Nick Craswell, An Introduction to Neural Information Retrieval, Foundations and Trends in Information Retrieval, vol. 13, no. 1, December 2018. (Click on "PDF" from within Columbia's network to access the full text of the article; alternatively, find the article in the "Files" section of CourseWorks, under "Readings.") Section 2.6 ("Neural Approaches to IR"), Section 3 ("Unsupervised Learning of Term Representations"), and Section 4 ("Term Embeddings for IR"), focusing on the topics covered in class and at the level of detail in the lecture.
  • Kaz Sato and Guangsha Shi, Your RAGs Powered by Google Search Technology, Part 1, Part 2, Google AI & Machine Learning Blog, February 2024
  • [OPTIONAL] Pandu Nayak, Understanding Searches Better than Ever Before, Google Search Official Blog, October 2019
  • [OPTIONAL] Jay Alammar, The Illustrated BERT, ELMo, and co.
  • [OPTIONAL] Jacob Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL-HLT 2019
  • [OPTIONAL] Tom Brown et al., Language Models are Few-Shot Learners, July 2020
  • [OPTIONAL] Jason Wei et al., Finetuned Language Models Are Zero-Shot Learners, February 2022
• Mar 5: Midterm exam (closed book, closed notes, covering first half of course).
• Mar 12: No lecture (Spring Recess).
• Mar 19: Mining Time-Series Data (lecture by Ioannis Paparrizos, Ohio State U.). On Zoom only.
  • Chotirat A. Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, Gautam Das: Mining Time Series Data. In Data Mining and Knowledge Discovery Handbook, pages 1049-1077, 2010 (focus on Sections 1, 2.1, 2.2, 3.1, 3.3, 4, 4.5, and 4.7)
• Mar 26, Apr 2: Data Mining.
  • Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets, 3rd edition, Cambridge University Press, 2019. Chapter 1 ("Data Mining")
  • Rakesh Agrawal and Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases, VLDB 1994
• Apr 9, Apr 16: Data Warehousing, OLAP, Decision Support.
  • Surajit Chaudhuri, Umeshwar Dayal, Vivek Narasayya: An Overview of Business Intelligence Technology, Communications of the ACM, Vol. 54 No. 8, Pages 88-98, August 2011. (Get the article from the Columbia Libraries using the link above; alternatively, find the article in the "Files" section of CourseWorks, under "Readings.")
• Apr 23: Spatial Data Management
• May 7, 9:30-11:00 a.m. ET: Final exam (closed book, closed notes, covering second half of course).