No classes on 09/02, 10/14 and 11/27 due to Labor Day Break, Fall Recess and Thanksgiving respectively.
Major course announcements will be
made available here and on GT Canvas
Lecture slides have been uploaded to Files section on Canvas
Include your preferred teammates for paper presentation in your "paper preference" email. We will try to accomodate that. Otherwise, students who express their interests in same paper will be asked to form a presentation group.
Note that paper presentation group can be different from Project group.
Big data processing poses many challenges, which are often
characterized by the three V's (volume, velocity, and variety). 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 covered topics
broadly categorized as follows:
Utilizing machine learning technologies to solve hard data
management challenges, such as data
Utilizing data management technologies to solve hard machine
learning challenges, such as model interpretation, debugging,
and feature engineering.
The course covers a wide range of moder challenges and
sub-topics in both data management and machine learning. The
students will get familiar with these sub-topics, and gain a
deep understanding of one sub-topic by doing presentations and
Office Hours: 3:30-4.30pm every Tue starting from Aug 20th
Appointments: E-mail me to book a
slot. The subject of the email should be "CS
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. References provide some relevant
courses and materials.