updated mmdii26 page

Author: tobideusser

teaching/mmdii26/index.md

@@ -4,20 +4,17 @@ title: Mining Media Data II
 description: Summer Semester 2026
 ---
 
-# Lecture: Mining Media Data II
+# Course: Mining Media Data II
 
-This course, offered as part of the **Master's Program in Media Informatics at the Bonn-Aachen International Center for Information Technology (B-IT)** as well as the **Master's Program in Computer Science at the University of Bonn**, explores advanced techniques in data mining, emphasizing predictive and prescriptive methods applied to media data. Students will learn to analyze large and complex datasets using state-of-the-art machine learning methodologies, including behavioral prediction, knowledge distillation, and large language models (LLMs). The curriculum includes foundational concepts, text representation learning, transformer architectures, and practical applications in media analytics, such as recommendation systems and information extraction. The course combines theoretical instruction with hands-on exercises to develop both technical and analytical skills relevant to industry and research.
+This course, offered as part of the *Master's Program in Human Centered Intelligent Systems and Media Informatics at the Bonn-Aachen International Center for Information Technology (B-IT)* as well as the *Master's Program in Computer Science at the University of Bonn*, explores advanced techniques in data mining, emphasizing predictive and prescriptive methods applied to media data. Students will learn to analyze large and complex datasets using state-of-the-art machine learning methodologies, including behavioral prediction, knowledge distillation, and large language models (LLMs). The curriculum includes foundational concepts, text representation learning, transformer architectures, and practical applications in media analytics, such as recommendation systems and information extraction. The course combines theoretical instruction with hands-on exercises to develop both technical and analytical skills relevant to industry and research.
 
-## Lecture topics
+## Course topics
 
-* Understand and implement advanced data mining techniques for predictive and prescriptive analytics.
-* Employ large language models and transformer-based architectures for tasks like text analysis, classification, and summarization.
-* Apply knowledge distillation techniques to optimize and deploy machine learning models in resource-constrained environments.
-* Analyze media data effectively to derive insights and support decision-making in real-world applications, including digital marketing and fraud detection.
-* Address challenges in media analytics, such as ethical considerations, model interpretability, and efficient resource use.
+* Understand and implement foundational models for a range of predictive and prescriptive data mining tasks.
+* Study advanced methods for training and extending learning systems using intelligent optimization techniques.
+* Analyze media data to derive actionable insights and support decision-making in real-world domains such as digital marketing, financial data analysis, and text/document analytics.
+* Tackle key challenges in media analytics, including ethical issues, model interpretability, and efficient use of computational resources.
 
 ## Lecturers
 
-- [Rafet Sifa](/rafetsifa/)
-
-
+- [Rafet Sifa](/rafetsifa/)