Data Management Challenges in Machine Learning (Fall 2018)
Big data processing poses many challenges, which are often characterized by the three V's (volume, velocity, and varity). On the other hand, machine learning is increasingly used by all kinds of data-driven applications.
This course explores the interactions between these two exciting fields.
This blogpost provides one perspective of such interactions.
Because of the purpose above, the course will be divided into two parts.
Utilizing machine learning technologies to solve hard data management challenges, such as data cleaning
Utilizing data management technologies to solve hard machine learning challenges, such as data representation and training data curation
Students should gain a much deeper understanding of modern challenges in both data management and machine learning.
Furthermore, since this is a graduate seminar, another important objective is to train students to master basic skills for being a researcher. The course will create a number of opportunities for students to learn how to read a paper, how to write a paper review, how to give a good research talk, and how to ask questions during a talk?
Office Hours: By appointment. E-mail me to book a slot
Students should have basic understandings of data analytics and machine learning. Though not required, an undergraduate course in relational database systems and an undergraduate course in machine learning would be helpful.
Grading (Subject to change)
Paper Presentation: 20%
Paper Review: 20%
Class Participation: 10%
Project: 50% (20% proposal + 20% poster or presentation + 60% final deliverables (code and report))