|Type||Core course, 9 ECTS|
Coordinators and Contact
(first lecture will be on Wednesday, Oct 16)
Please select your tutorial slot here: https://forms.gle/qjhu7jwkaRHGNWFM8
|Exams||To be announced|
|Lecture 01||16.10.19||Foundations I||RSR||Aggarwal Ch. 2|
|Lecture 02||18.10.19||Foundations II||RSR||Aggarwal Ch. 12|
|Lecture 03||23.10.19||Statistics I||AY|
|Lecture 04||25.10.19||Statistics II||AY|
|Lecture 05||30.10.19||Pattern Mining I||RSR|
|Lecture 06||06.11.19||Pattern Mining II||RSR|
|Lecture 08||13.11.19||Clustering I||JV|
|Lecture 09||15.11.19||Clustering II||JV|
|Lecture 10||20.11.19||Sequences I||RSR|
|Lecture 11||22.11.19||Sequences II||RSR|
|Lecture 12||27.11.19||Graphs I||RSR|
|Lecture 13||29.11.19||Graphs II||RSR|
|Lecture 14||04.12.19||Anomaly Detection||RSR|
|Lecture 15||06.12.19||IR Basics||AY|
|Lecture 18||18.12.19||Ranking I||AY|
|Lecture 19||20.12.19||Ranking II||AY|
|Lecture 20||08.01.20||Click Analysis I||RSR|
|Lecture 21||10.01.20||Click Analysis II||RSR|
|Lecture 22||15.01.20||Neural IR I||AY|
|Lecture 23||17.01.20||Neural IR II||AY|
|Lecture 24||22.01.20||Query expansion||AY|
|Lecture 25||24.01.20||Entities in IR||AY|
|Lecture 26||29.01.20||Question Answering||RSR|
|Lecture 24||31.01.20||Recap||RSR, AY|
Information Retrieval (IR) and Data Mining (DM) are methodologies for organizing, searching and analyzing digital contents from the web, social media and enterprises as well as multivariate datasets in these contexts. IR models and algorithms include text indexing, query processing, search result ranking, and information extraction for semantic search. DM models and algorithms include pattern mining, rule mining, classification and recommendation. Both fields build on mathematical foundations from the areas of linear algebra, graph theory, and probability and statistics.
Good knowledge of undergraduate mathematics (linear algebra, probability theory) and basic algorithms.
After you receive the assignment sheet, you solve the problems (individually) at home, and submit them on the appointed dates to the TAs after the lecture. During the tutorial sessions, the TAs will ask some of you to present your solutions. Every student must present their solutions at least 2 times during the semester. The TAs will also help in clarifying your answers. Your submitted sheets will be graded and handed back to you at the end of the session.
To do the exercises, you have to study the required reading material and go through the slides.
We do not allow plagiarism. The first time you are caught, you will receive 0 points for the specific assignment. The second time, you will be de-registered from the course.
The overall grade will be the best result of the end-term and a re-exam (there will be no further attempts). There will be no mid-term exams. The final exam is closed-book and no discussion is allowed.
To participate in the final written exam, the following prerequisites are required:
- Submit ALL 14 assignments
- Obtain 50% or more on average over all assignments (80% or more on average will fetch you a bonus point, that results in one grade point jump (if possible) in the final exam)
- Present solutions at least 2 times in the tutorials
We will use the following primary textbooks.
For Probability and Statistics,
- Larry Wasserman: All of Statistics, Springer, 2004.
For Data Mining,
- Charu Aggarwal: Data Mining - The Textbook, Springer, 2015.
- Mohammed J. Zaki and Wagner Meira Jr: Data Mining and Analysis, Cambridge University Press, 2014.
For Information Retrieval,
- Chris Manning, Prabhakar Raghavan, and Hinrich Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008.
- ChengXiang Zhai and Sean Massung: Text Data Management and Analytics, Morgan & Claypool, 2016
These and additional references are available in the library: