This course introduces the key concepts and algorithms from the areas of information retrieval, data mining and knowledge bases, which constitute the foundations of today’s web-based distributed information systems.
Class
Platform | Description |
---|---|
Lecture | Thursdays [10:15-12:00]: Live Webinar, QnA, In-Lecture Quizzes [link] |
Lab session | Thursdays [12:15-14:00] |
Mediaspace | Recorded Lectures [link] |
Ed Forum | Q&A [link] |
Moodle | Quizzes, Final Exam [link] |
Schedule
Week | Date | Exam (TBA) | Area | Material |
---|---|---|---|---|
1 | 12/09 | Course overview | [slides] | |
2 | 19/09 | Information Retrieval | Basic Information Retrieval | |
3 | 26/09 | Embedding techniques | ||
4 | 03/10 | Embedding techniques | ||
5 | 10/10 | Recommender Systems | ||
6 | 17/10 | Document Classification | ||
7 | 31/10 | Link Ranking | ||
8 | 7/11 | Information Extraction | Graph Mining | |
9 | 14/11 | Named Entity Recognition | ||
10 | 21/11 | Knowledge Representation | ||
11 | 28/11 | Information Extraction | ||
12 | 05/12 | Knowledge Inferences | ||
13 | 12/12 | Indexing for Information retrival ] | ||
14 | 19/12 | Association Rule Mining |
Projects [video]
During the semester, you will need to implement 2 graded projects, regarding the following topics:
- P1: Text Retrieval
- P2: TBA
Logistics:
- All 2 projects comprise 50% of the final grade (25% each).
- You will have 6 weeks to implement and submit each project (please review the detailed schedule per project below).
- You will have 5 late-days policy that you can use in any project deadline throughout the semester.
- All projects will be implemented in Python.
- A 2-page report of each project should be submitted in Moodle.
- Details for each project will be released for each project respectively.
P1: Text Retrieval [video]
Release Date: 16 Sept
Presentation Date: 19 Sept, 12-13:00 (during lab session)
Deadline Date: 3 Nov, 23:59.
P2: TBA
Release Date: 4 Nov
Presentation Date: 7 Nov, 12-13:00 (during lab session)]
Deadline Date: 15 Dec, 23:59.
Evaluation criteria
- 40%: Results - Metrics [Comparison with baselines]
- 30%: Code
- Working code (20%)
- Code quality and documentation (10%)
- 30%: 2-page Report [Moodle submission]
- Originality of approach (10%)
- Interpretation of results (10%)
- Report presentation & clarity (10%)
Exercises
Exercises are NOT graded. We have released exercises for all topics covered in the weekly lectures under this path. Feel free to solve them since the final exam will follow the same format.
Exercises / Lab sessions will be allocated for questions regarding the Projects.
Final Exam (TBA)
Communication Guidelines
Q&A: Questions to TAs should be asked only in the dedicated per-topic folders in the Ed forum. TAs will be available in the zoom session only during the exercise sessions. TAs won’t provide answers via email.
Best Practices: Please join the Ed forum with your academic email. Before posting a question, it is important to check if it has already been answered. Set your full name as your nickname. Please keep the communication respectful.
Contacts
Lecturer: Karl Aberer. You can contact me anytime by email. If necessary, I will schedule a Zoom meeting to clarify critical questions.
Teaching assistants: Angelika Romanou, Negar Foroutan, Beatriz Borges and Mete Ismayilzada. Please contact us for any organisational questions or questions related to the course content.