Information Retrieval and Web Search (FSS2018)

News

First lecture

The first lecture of the IR&WS course in FSS18 will be held on Monday, February 12 at 13.45 in room A1.01.   

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General description

Level: Master (Diploma)

Prerequisites:  

  • Programming skills (a higher-level pgoramming languages like Java, Python, C#, or C++ recommended).
  • Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures

Description:

Given the vastness and richness of the Web, users need high-performing, scalable and efficient methods to access its wealth of information and satisfy their information needs. As such, being able to search and effectively retrieve relevant pieces of information from large text collections is a crucial task for the majority (if practically not all) of Web applications. In this course we will explore a variety of basic and advanced techniques for text-based information retrieval and Web search. Coursework will include homework assignments (exercises), a term project and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam. In addition, students are expected to successfully complete a term project in teams of 2-4 people. The projects will focus on a variety of IR problems covered in class. Project deliverables include both software (i.e., code and documentation) and a short report explaining the work performed and its evaluation.

Organization

Teaching staff:

Lectures: On Mondays, starting at 13.45 am (approximately 1.5 hours lecture session, followed by an exercise session)

Office hours:

1. Goran: Fridays in lecture weeks at 15.00, B6 26, Building C, Room C1.02 (previous announcement via email)

2. Robert: to be announced

Lectures period:

  • First session: February 12, 2018
  • Last session (project presentations): May 28
  • No sessions: March 26, April 2 (Easter break); May 21 (Whit Monday holiday)

Course materials: Include lecture slides and exercise/homework assignment sheets. All materials will be posted in the ILIAS page of the course, at least one week in advance. 

Course schedule

  • Lecture 01 (Feb 12): Introduction to Information Retrieval
  • Lecture 02 (Feb 19): Boolean Retrieval and Term Indexing
  • Lecture 03 (Feb 26): Data Structures in IR and Tolerant Retrieval
  • Lecture 04 (Mar 5): Term Weighting and Vector Space Model
  • Lecture 05 (Mar 12): Probabilistic Information Retrieval
  • Lecture 06 (Mar 19): Language Modelling for Information Retrieval
  • Lecture 07 (Apr 9): Relevance Feedback and Query Expansion
  • Lecture 08 (Apr 16): Latent and Semantic Information Retrieval Models
  • Project coaching session (Apr 23) 
  • Lecture 09 (Apr 30): Classification, Clustering, Learning to Rank, and IR Evaluation 
  • Lecture 10 (May 7): Web Search and Link Analysis
  • Project coaching session (May 14) 
  • Project presentations session (May 28)

Grading / Evaluation

●     50% final exam

●     50% final project

 

Final Exam. Coursework will include homework assignments: these are meant to give you a reasonable idea of the topics and exercises that will be covered in the final exam at the end of the course.

Final Project. Students are expected to successfully complete a team project in teams of 3 people. The projects will focus on a variety of IR models covered in lectures. Project deliverables include both software (i.e., code and documentation) and a short report (about 5 pages) explaining the work and evaluation.

Textbooks

C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008 (available at http://nlp.stanford.edu/IR-book).

B. Croft, D. Metzler, T. Strohman, Search Engines: Information Retrieval in Practice, Addison-Wesley, 2009 (available at  http://ciir.cs.umass.edu/irbook/ ).

R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 2011 (2nd Edition).