Human-in-the-loop Data Analytics
CS8803-MDS, Fall 2023
Instructor: Kexin Rong
Time: Mon & Wed 5-6:15PM
Location: Clough Rm 131
TAs: Ashmita Raju, Saumia Singhal
Course Description: Humans play an important role in almost all stages in the data analytics lifecycle. In turn, data management and analytics can be made more effective when taking into account the people who design and build these processes as well as those who are impacted by their results. This course explores different roles of humans in the analytics life cycle, including data scientists, data producers and data consumers.
Human-in-the-loop data analytics is a nascent field at the intersection of Databases, Machine Learning and HCI. Students will gain a deep understanding of cutting-edge research through reading and presenting research papers while performing their own original research through course projects.
Office Hours
- Instructor: Thursdays 4-5PM, KACB 3322
- TA: Fridays 2-3PM, outside KACB 3319
Prerequisites: Though not required, an undergraduate course in relational database systems and an undergraduate course in machine learning would be helpful.
Grading Policy
This course has no midterm or final exams. You will be graded on the basis of your class participation and course projects. We will read one papers for each class, and discuss them in class. Before class, you are expected to submit a short review of the required readings. Each class will also have one or more presenters who are in charge of leading the dicussions. Another significant portion of the grade comes from a semester-long course project, where you can work in a team of 1-3 people on a research project that is related to the course topics.
The final grade for the course will be tentatively based on the following weights:
- Participation: 45%
- Class Participation: 10%
- Paper Review: 15%
- Paper Presentations: 20%
- Assignment: 10%
- Course Project: 45%
- Project Proposal: 4%
- Progress Report: 6%
- Draft Paper + Peer Review: 5%
- Final Project Presentation: 10%
- Final Paper: 20%
Assignments that are submitted late, without prior communication and agreement with the course staff, will receive a penalty. The late penalty is a deduction of 25% from the total possible points for the assignment.
General Policy
You are encouraged to discuss the required readings with your classmates. However, the paper reviews must be written by yourself. For paper presentations, you are encouraged to reference the authors’ slides, but you can not directly use their slides without any modification. You are expected to abide by the Georgia Tech Honor Code.