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* Lecture 10, Tuesday, January 14, 2014 (Naive Bayes) | * Lecture 10, Tuesday, January 14, 2014 (Naive Bayes): [[https://daphne.informatik.uni-freiburg.de/svn/InformationRetrievalWS1314/public/slides/lecture-10.pdf|Slides]], [[https://daphne.informatik.uni-freiburg.de/svn/InformationRetrievalWS1314/public/exercises/sheet-10.pdf|Exercise Sheet 10]], [[https://daphne.informatik.uni-freiburg.de/svn/InformationRetrievalWS1314/public/code/lecture-10/NaiveBayes.TIP|Implementation advice for this sheet]], [[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1314/people.with-10-labels.txt|Dataset (50,562 abstracts about people, with class labels)]], [[InformationRetrievalWS1314/ResultsClassification|Table for your classification results]]. |
Welcome to the Wiki of the course '''Information Retrieval''' in the winter term 2013/2014
The course is given by Prof. Dr. Hannah Bast and assisted by Björn Buchhold. It takes place every Tuesday from 4:15pm until 5:45pm in the Hörsaal HS036 in building 101. The first lecture is on Tuesday, October 22, 2013 and the last lecture is on Tuesday, February 11, 2014. There will be no lecture on Tuesday, December 24, 2013 and Tuesday, December 31, 2013 (christmas break) and on Tuesday, Januar 7, 2014. That is 14 lectures altogether.
The tutors for this course are Stephanie Embgen (s.embgen@gmail.com), Jonas Sternisko (sternis@informatik.uni-freiburg.de) and Simon Skilevic (skilevis@informatik.uni-freiburg.de). 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).
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 to those of the course Information Retrieval, WS 2012/2013 from last year. Here is all about the Exam from that course.
Lecture Slides, Video Recordings, Exercise Sheets, and Code
Lecture 1, Tuesday, October 22, 2013 (Demo, Organizational, Inverted Index): Video Recording (Download), Slides, Exercise Sheet 1, Code from the lecture, Code design suggestion for ES1, Dataset for ES1 (961,327 abstracts about people), Solution for the exercise sheet.
Lecture 2, Tuesday, October 29, 2013 (Ranking, Vector Space Model, Evaluation): Video Recording (Download), Slides, Exercise Sheet 2, TIP files for ES2, Table for your ranking results, Solution for the exercise sheet.
Lecture 3, Tuesday, November 5, 2013 (Efficient List Intersection): Video Recording (Download), Slides, Exercise Sheet 3, Code from the lecture, Table for your list intersection results, Solution for the exercise sheet.
Lecture 4, Tuesday, November 12, 2013 (Compression and Entropy): Video Recording (Download), Slides, Exercise Sheet 4, Complete code for VB-encoding (for both Java and C++), Table for your compression results, Solution for the exercise sheet.
Lecture 5, Tuesday, November 19, 2013 (Fuzzy Search, Edit Distance, q-Gram Index): Video Recording (Download), Slides, Exercise Sheet 5, Dataset for ES5 (8.2M entity names from Freebase, with popularity scores),Code for the PED + TIP files, Table for your q-gram index results, Solution for the exercise sheet.
Lecture 6, Tuesday, November 26, 2013 (How to build a web application): Video Recording (Download), Slides, Exercise Sheet 6, Basic server code, Solution for the exercise sheet.
Lecture 7, Tuesday, December 3, 2013 (Cookies, CORS, UTF-8): Video Recording (Download), Slides, Exercise Sheet 7, Dataset for ES7 (The entity names with scores from sheet 5 and 6 encoded in ISO-8859-1), Solution for the exercise sheet.
Lecture 8, Tuesday, December 10, 2013 (Synonyms, Latent Semantic Indexing): Video Recording (Download), Slides, Exercise Sheet 8, Alwis (Windows installer), Solution for the exercise sheet.
Lecture 9, Tuesday, December 17, 2013 (Clustering, K-Means): Video Recording (Download), Slides, Exercise Sheet 9, Invaluable implementation advice for this sheet, Dataset (100.000 abstracts about people), Table for your clustering results.
Lecture 10, Tuesday, January 14, 2014 (Naive Bayes): Slides, Exercise Sheet 10, Implementation advice for this sheet, Dataset (50,562 abstracts about people, with class labels), Table for your classification results.