Schema della sezione

  • Introduzione

    Minimizza tutto Espandi tutto

  • Information Retrieval

    The Information Retrieval part of the course will be structured with both in-person and online lectures. The first lecture will be on October 9, 2020 and it will be an in-person lecture.

    • Monday, from 9 am to 11am (online).
    • Friday, from 9am to 11am, Room 5A, H2bis building (in-person).


    The course has moved to a full Team (instead of only a chat). To join it use the following link: https://teams.microsoft.com/l/team/19%3adfb68332f938413996640dd43c306367%40thread.tacv2/conversations?groupId=a58f9ecb-d21b-4fc2-b2a5-621c0c0da6a5&tenantId=a54b3635-128c-460f-b967-6ded8df82e75

    The Team of the course can be accessed here.

    The recording of the lectures are available here: 


    Important information on the exam

    As a general rule the date of the exam can per personalised for each person.
    However, to help with coordination, for December and January there are two fixed dates: 

    • December 18, 2020. Please submit the project (code or report) by email on or before December 15.
    • January 15, 2021. Please submit the project (code or report) by email on or before January 12.

    • Doodle to select the IR Project URL

      Select which project you would like to prepare for the IR part of the course. If two/three people select the same project then it must be split into two/three distinct projects (see slides). Write an email to ask for a personalised project.

    • Introduction to Information Retrieval URL

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

      This is textbook for the course, the pdf and html versions of the book are available from the linked website


    • Lecture 1 - Slides File PDF

      Topics:

      • Introduction to the course
      • Exam info
      • History of IR
      • Terminology
      • Boolean model
      • Inverted Index

    • Lecture 2 - Slides File PDF

      Topics:

      • Answering queries using the inverted index
      • Stemming and lemmatization
      • Stop words
      • Answering phrase queries: biword index and positional posting
      • Data structures for posting lists

    • Lecture 3 - code File ZIP

      Python Notebook and python source code of a simple Boolean Information Retrieval System.

      The corpus is available at: http://www.cs.cmu.edu/~ark/personas/.

    • Lecture 4 - Slides File PDF

      Topics:

      • Data structures for dictionaries (hash tables, b-trees, tries)
      • Postings list and dictionary compression
      • Updating the index

    • Lecture 5 - Slides File PDF

      Topics:

      • Wildcard queries
      • Spelling corrections
      • Query optimization

    • Lecture 5 - code File ZIP

      Code to implement spelling correction (with edit distance) in a simple Boolean IR system.

    • Lecture 6 - Slides (updated) File PDF

      Topics:

      • Ranked retrieval
      • Zone scoring
      • tf-idf weighting
      • Vector space model

    • Lecture 7 - Slides File PDF

      Topics:

      • Evaluation of IR systems
      • Relevance feedback

    • Lecture 7 - code File ZIP

      Code to implement the ranking of the results via tf-idf

    • Lecture 8 - Slides [updated] File PDF

      Topics:

      • Probabilistic IR
      • Binary Independence Model
      • OKAPI weighting
      • Bayesian networks

    • Projects [Updated] File PDF

      List of possible projects for the IR course. Two new types of projects have been added.

    • Lecture 9 - Slides [Updated] File PDF

      Topics:

      • Structure of the web
      • Crawling
      • IR on the web
      • Storage

    • Lecture 10 File PDF

      Topics:

      • PageRank 
      • HITS

    • Lecture 11 - collections File ZIP

      The TIME collection and a simple graph in json format. The two files will be used in Lecture 11.

    • Lecture 11 - code File ZIP

      A way to show vectors in the vector space model and a simple PageRank implementation.

    • Lecture 12 - Slides File PDF

      Topics:

      • Matrix factorisation
      • Latent Semantic Indexing
      • Recommender systems

    • Additional Material File PDF

      Topics:

      • Multimedia IR
      • IR and Neural Networks
      • Where to go next

  • Data Visualization

    The lectures on Data Visualization start on Friday, November 20. We will continue with the established schedule, that is Monday and Fridays from 9:00 to 11:00. All lectures will be online only. You can access the Teams here.

    All students that were members of the Information Retrieval Team are also members of the Data Visualization Team. If a mistake was made and this is not the case, let me know.