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| * Lecture 2, Tuesday, October 25, 2016 (Ranking and Evaluation): [[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1617/lecture-02.avi|Video Recording (raw version, final cut later today)]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/slides/lecture-02.pdf|Slides]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/exercises/sheet-02.pdf|Exercise Sheet 2]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn/public/code/lecture-02/sheet-02.TIP|TIP file]] and [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn/public/code/lecture-02/example.txt|example file]] for test cases, [[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1617/movies.txt|Movies dataset (same one as for ES1)]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/code/lecture-02/movies-benchmark.txt|Movies Benchmark]], [[InformationRetrievalWS1617/ResultsES2|Table for your ranking results]]. | * Lecture 2, Tuesday, October 25, 2016 (Ranking and Evaluation): [[https://youtube.com/embed/zpORmBIAmOE|Video Recording]] ([[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1617/lecture-02.mp4|Download]]), [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/slides/lecture-02.pdf|Slides]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/exercises/sheet-02.pdf|Exercise Sheet 2]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn/public/code/lecture-02/sheet-02.TIP|TIP file]] and [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn/public/code/lecture-02/example.txt|example file]] for test cases, [[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1617/movies.txt|Movies dataset (same one as for ES1)]], [[https://daphne.informatik.uni-freiburg.de/ws1617/InformationRetrieval/svn-public/public/code/lecture-02/movies-benchmark.txt|Movies Benchmark]], [[InformationRetrievalWS1617/ResultsES2|Table for your ranking results]]. |
Welcome to the Wiki of the course "Information Retrieval" in the winter term 2016/2017
The course is given by Prof. Dr. Hannah Bast and assisted by Patrick Brosi. It takes place every Tuesday from 4:15pm until 5:45pm in the Hörsaal HS 026 in building 101. The first lecture is on Tuesday, 18 October 2016 and the last lecture is on Tuesday, 7 February 2017. There will be no lecture on Tuesday, 1 November 2016 (Halloween), and on Tuesday, 27 December 2016 and Tuesday, 3 January 2017 (christmas break). This is 14 lectures altogether.
The tutors for this course are Raghu Rajan, Natalie Prange, Johanna Götz, Axel Lehmann, Claudius Korzen, Björn Buchhold. The administrator of the supporting systems (Daphne, SVN, Forum, Jenkins) is Axel Lehmann.
Important Links
Here is the link to Daphne (our course management system, login with your RZ user name and password).
Here is the link to the Forum (for announcements and questions, you are automatically a member when you have logged into Daphne).
Code style profiles for Eclipse: Java, C++ (add .xml to file name).
Short introductions to SVN and Vim and the installation of gtest (for C++ only) can be found here (in German).
The topics of this course will be similar (but not identical) to those of the courses Information Retrieval WS 2012/2013 and Information Retrieval WS 2013/2014 and Information Retrieval WS 2015/2016.
Here is information about the exams from previous years: WS 12/13, WS 13/14, WS 15/16.
Lecture Slides, Video Recordings, Exercise Sheets, and Code
Lecture 1, Tuesday, October 18, 2016 (Introduction, Inverted Index, Zipf's Law): Video Recording (Download), Slides, Exercise Sheet 1, Code from the lecture, Dataset for ES1 (189,898 movies, title + description), Solution.
Lecture 2, Tuesday, October 25, 2016 (Ranking and Evaluation): Video Recording (Download), Slides, Exercise Sheet 2, TIP file and example file for test cases, Movies dataset (same one as for ES1), Movies Benchmark, Table for your ranking results.