Mathematics Models in Advanced Information Retrieval - 096231
Will be learned this year
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|
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Lecture |
Exercise |
Laboratory |
Project or
Seminar |
House
Work |
Weekly
Hours |
3 |
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4 |
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Determination of the grade according to progress during the semester and the submission of the final thesis
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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 time | day | date | Season |
|---|
| | Sunday | 04.07.2021 | à |
| | Friday | 08.10.2021 | á |
Timetable to semester 02/2020
2020/2021 Spring Semester
| Room | Building | Hour | day | Lecturer | Exercise
Lecture | no. | Registering
Group |
|---|
| | | 11:30-14:30 | Tuesday | Full Professor Kurland Oren | Lecture | 10 | 10 |
Textbooks| Published | Publisher | Authors | Book |
|---|
| 2013 | morgan and claypool | t. rolleke | ir models:foundations and relationships |
| 2009 | springer | t. liu | learning to rank for information retrieval |
Created in 08/03/2021 Time 14:38:52
