Welcome to Data Integration and Machine Learning: A Natural Synergy
Description: There is now more data to analyze than ever before. As data volume and variety have increased, so have the ties between machine learning and data integration become stronger. For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This PhD course focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data integration and machine learning.
Prerequisites: Basics about ML and databases.
The goal of this course is to delineate the interplay between modern data integration techniques and modern machine learning. Specifically, we review (1) how recent advancements in machine learning (such as highly-scalable inference engines and deep learning) are revolutionizing data integration, and (2) how incorporating data integration tasks in machine learning pipelines leads to more accurate and usable systems for analytics. This course will highlight the strong connections between data integration and machine learning, review related technical challenges and recent solutions, and outline open problems that remain to be solved.
Organizer: Associate professor, Hua Lu, email@example.com
Lecturers: Xin Luna Dong (Amazon)
Time: June 27-28, 2019
Number of seats: 30
Deadline: June 6th, 2019
Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
- Teacher: Hua Lu