LEARNING OUTCOMES
In the age of big data and artificial intelligence, proficiency in database management is essential for leveraging the power of machine learning algorithms. This undergraduate course provides students with a comprehensive understanding of databases and their pivotal role in supporting machine learning applications. Through a blend of theoretical concepts, practical exercises, and real-world case studies, students will explore the principles, methodologies, and best practices for integrating databases with machine learning workflows.
Credits: 5
Schedule: 15.04.2025 - 30.05.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Alex Jung
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
* Introduction to Databases and Machine Learning:
* Relational Databases and SQL for Machine Learning:
* NoSQL Databases and Big Data Technologies
* Data Preprocessing and Feature Engineering:
* Database Integration with Machine Learning Frameworks:
Assessment Methods and Criteria
valid for whole curriculum period:
Assignemtns and Quizzes.
Workload
valid for whole curriculum period:
Lectures 69h, independent work 59h.
DETAILS
Study Material
valid for whole curriculum period:
A. Jung, "Machine Learning: The Basics," Springer, 2022.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring IV - V
2025-2026 Spring IV - V