Subject: Subject Sylbus: Mathematics Models in Advanced Information Retrieval - 096231 (Current)

Mathematics Models in Advanced Information Retrieval - 096231
Will be learned this year
Credit
Points
3.0
Lecture Exercise Laboratory Project or
Seminar
House
Work
Weekly
Hours
3 4

Determination of the grade according to progress during the semester and the submission of the final thesis


Prerequisites: Information Retrieval 096262


Advanced Probabilistic Retrieval Methods, Advanced Stochastic Language Modeling Techniques, Term-Proximity Models, Results Diversification Information-Theoretic Retrieval Models, the Risk-Reward Paradigm, Rank-Aggregation/Fusion, Using Topic Models for Retrieval. Models, the Axiomatic Approach to Retrieval,

Learning Outcomes
At the End of the Course the Student Will
1. Know How to Adapt and Apply the Matehamtical Models Taught in Class to New Problems, Be Able to Create Variants of the Models That Were Thught in Class for the Same Problems.
2. Design An Em Algorithm for Performing Soft Clustering. Example.
3. Design a Risk-Reward Algorithm for Diversifying Search Engine'S Results.




Times and places of examinations 02/2020 2020/2021 Spring Semester
examination timedaydateSeason
Sunday04.07.2021
Friday08.10.2021

Timetable to semester 02/2020 2020/2021 Spring Semester
RoomBuildingHourdayLecturerExercise
Lecture
no.Registering
Group
11:30-14:30TuesdayFull Professor Kurland OrenLecture1010


Textbooks
PublishedPublisherAuthorsBook
2013morgan and claypoolt. rollekeir models:foundations and relationships
2009springert. liulearning to rank for information retrieval

Created in 08/03/2021 Time 14:38:52