The class is centered around multiple research sub-topics in the areas of ``DB for ML'' and ''ML for DB'', such as how to use ML for data cleaning and data integration, and how to manage (i.e., label, clean, and de-bias) training data for ML.
The instructor will introduce all research topics for the first half of the semester. The students will present research papers and their research projects for the second half of the semester.
Disclaimer: The schedule could be subject to
change as the semester progresses.