Added the updated version of MMDI for WS26/27

Author: Onkeljoe1234

teaching/index.md

@@ -41,7 +41,7 @@ Practical Examples will serve as a capstone to the course, where students will e
 
 # Course: Mining Media Data I
 
-See [here](./archive/mmdi25/) for the archived iteration (Winter Semester 2025/2026) of this course.
+See [here](./mmdi2627/) for the current iteration of this lab.
 
 This course, offered as part of the Master's Program in Media Informatics at the Bonn-Aachen International Center for Information Technology (B-IT), provides a comprehensive exploration of advanced data mining techniques tailored for media data analysis. Students will delve into methods like affinity mining, latent pattern mining, neural networks, and archetypal analysis to uncover insights in behavioral profiling, recommender systems, and outlier detection. Emphasis is placed on theoretical understanding and practical application through mathematical optimization, interpretable models, and real-world case studies, enabling participants to harness data for impactful digital marketing, fraud detection, and content personalization.
 

teaching/mmdi2627/index.md

@@ -0,0 +1,24 @@
+# Mining Media Data I WS26 (MA-INF 4117)
+
+Lectures will take place every Wednesday 15:00-16:30 at the B-IT, room 2.113 (Friedrich-Hirzebruch-Allee 6, 53115 Bonn, Germany). The first lecture will take place on 14.10.26. Exercises will take place on selected Wednesdays from 13:00-14:30 at the B-IT, room 0.108. The course has 4-ETCS credits and is taught by Prof. Dr. Rafet Sifa/Dr. Lorenz Sparrenberg.
+
+|                 Lecture | Date    | Lecure Content              |
+| ----------------------: | ------- | --------------------------- |
+|               Lecture 1 | 14. Oct | Welcome/Organization        |
+|               Lecture 2 | 21. Oct | Affinity Mining I           |
+|               Lecture 3 | 28. Oct | Affinity Mining II          |
+|               Lecture 4 | 04. Nov | Latent Pattern Mining I     |
+|               Lecture 5 | 11. Nov | Latent Pattern Mining II    |
+|               Lecture 6 | 18. Nov | Latent Pattern Mining III   |
+|               Lecture 7 | 25. Nov | Gradient Decent             |
+|               Lecture 8 | 02. Dec | Outlier Analysis            |
+|               Lecture 9 | 09. Dec | Preditctive Data Mining I   |
+|                         | 16. Dec | No Lecure                   |
+|                         | 23. Dec | No Lecure                   |
+|                         | 30. Dec | No Lecure                   |
+|                         | 06. Jan | No Lecure                   |
+|              Lecture 10 | 13. Jan | Preditctive Data Mining II  |
+|              Lecture 11 | 20. Jan | Preditctive Data Mining III |
+|              Lecture 12 | 27. Jan | Recap                       |
+|              Exam 1     | 24. Feb | First take                  |
+|              Exam 2     | 31. Mar | Second take                 |