Course in Information Retrieval
General Information
| Lecturer | Jun.-Prof. Dr. Martin Potthast |
| Lab Advisors | Lukas Gienapp, Christopher Akiki |
| Workload | 2 SWS Lecture, 1 SWS Lab |
| Lecture | Monday, 11:15 - 12:45, starting 12.04.2021, online |
| Lab | Tuesday, 9:15 - 10:45, starting 12.04.2021, online |
| Contact | Email, or via Discord server "irlecture" |
| Exam | Written online exam, Monday, 19th of July, 11:00. |
Announcements
- Exam will take place on Monday, 19th of July, 11:00. Technical information on how the exam will be conducted will follow shortly.
Organization
- Lectures are prerecorded. The videos can be accessed by following the lecturenotes below, or on the Webis youtube channel. [playlist]
- Online sessions will take place in the form of live Q&A sessions on BigBlueButton. These sessions are planned semi-regularly, each date will be announced on the course website.
- Lab material consists of biweekly programming exercises in the form of Jupyter notebooks.
- Examination will take place as online written exam via Moodle on Monday, 19th of July, 11:00.
- Communication
- Lecture website - materials and announcements will be uploaded on this website.
- Discord - there is a dedicated Discord server for this lecture to ask questions and engage in discussion. Check your mails for an access code. Please join the server and choose a Nickname such that we can identify you (at least surname).
- Email - important announcements will be sent out via mail.
Lecturenotes
- Information Retrieval » Introduction » Organization, Literature [video 1]
- Information Retrieval » Introduction » Retrieval Problems [video 2] [video 3] [video 4] [video 5] [video 6]
-
Information Retrieval »
Introduction »
Architecture of a Search Engine
[video 7]
[video 8]
[video 9]
[video 10]
[video 11]
[video 12]
[video 13]
[video 14]
- Natural Language Processing » Words » Text Preprocessing [video 15]
-
Natural Language Processing »
Words »
Morphological Analysis
[video 16]
- Information Retrieval » Indexing » Indexing Basics [video 17]
-
Information Retrieval »
Indexing »
Inverted Indexes
[video 18]
[video 19]
[video 20]
[video 21]
- Information Retrieval » Retrieval Models » Overview of Retrieval Models [video 22]
- Information Retrieval » Retrieval Models » Empirical Models [video 23]
- Machine Learning » Statistical Learning » Probability Basics [video 24] [video 25]
- Machine Learning » Statistical Learning » Bayes Classification [video 26]
- Information Retrieval » Retrieval Models » Probabilistic Models [video 27] [video 28]
-
Information Retrieval »
Retrieval Models »
Generative Models
[video 29]
- Information Retrieval » Evaluation » Laboratory Experiments [video 30]
- Information Retrieval » Evaluation » Effectiveness Measures [video 31] [video 32] [video 33]
-
Information Retrieval »
Evaluation »
Training and Testing
[video 34]
- Information Retrieval » IR Applications » Argument Search [video 35] [video 36] [video 37] [video 38] [video 39]
Lab
The lab will be conducted as a series of exercises to give a hands-on experience for the concepts taught in the lecture. Each lecture block will be accompanied by a Jupyter notebook, which implements a component of a basic search engine.
Exercises will be published here every two weeks, with the solution following one week later. We will not collect & grade your exercise solutions, and participation in lab is not a prerequisite to the exam.
Lab Sessions
- 2021/04/26 - Session 01: Exercises 01 and 02
- 2021/05/10 - Session 02: Exercise 03
- 2021/05/31 - Session 03: Exercise 04
- 2021/06/14 - Session 04: Exercise 05
- 2021/06/28 - Session 05: Exercise 06
Lab Exercises
- 00 - Getting Started [download]
- 01 - Introduction to Python [view] [download]
- 02 - Introduction to Jupyter [view] [download]
- 03 - Text Analysis [view notebook] [download notebook] [download data] [view solution] [download solution]
- 04 - Indexing [view notebook] [download notebook] [download preprocess.py] [view solution] [download solution]
- 05 - Basic Retrieval [view notebook] [download notebook] [download preprocess.py] [download indexing.py] [download shakespeare.py] [view solution] [download solution]
- 06 - Advanced Retrieval [view notebook] [download notebook] [download preprocess.py] [download indexing.py] [download shakespeare.py] [view solution] [download solution]
- 06 - Evaluation [view notebook] [download notebook] [download preprocess.py] [download indexing_np.py] [download models.py] [download system.py] [download data] [view solution] [download solution]