Graph Machine Learning Foundations and Applications (AI60007) / Autumn,2022
Course Description
Graphs have widely been used in representing problem scenarios in different domains. Subsequently, several computational models have been developed to process graph representations. Machine learning, like in many other domains, have been used to model different tasks on graphs including graph clustering, node classification, link prediction etc. Deep learning models though are proven to be very effective in domains like computer vision, text processing etc., cannot be directly applicable in irregular data representation like graphs. Subsequently, a new paradigm of machine learning (graph machine learning) has emerged. Right from its emergence, it has been widely adopted in diverse domains including social networks, protein-protein interaction networks, drug disease interactions etc. Along with its effectiveness on graph data, graph machine learning has also improved the state-of-art in the domains (e.g., computer vision, text processing) where traditional deep learning architectures have ruled. Considering the rapid adoption of graph machine learning and its effectiveness, this course is proposed to develop the theoretical foundation of graph machine learning models and their use in real world applications in diverse domains.
Course Logistics
- Lecture Hours:
Thursday [03:00 PM to 04:55 PM] (2 Hrs)andFriday [03:00 PM to 03:55 PM] (1 Hr). - Classroom: Classes will happen in person in the
NC-421. - L-T-P & Credits:
3-0-0&3 Credits.
Mid-Semester Exam Schedule
- Exam Date:
Thursday, 29-09-2022. - Time Slot:
09:00 AM to 11:00 AM (2 Hrs). - Venue:
NC-122.
End-Semester Exam Schedule
- Exam Date:
Friday, 25-11-2022. - Time Slot:
02:00 PM to 05:00 PM (3 Hrs). - Venue:
NC-322.
Grading Policy
- Semester Examinations:
- Mid-Semester Examination: [30%]
- End-Semester Examination: [40%]
- Assignment: [10%]
- Project: [20%]
Prerequisites
- Deep Learning: Foundations and Applications (AI61002) or Deep Learning (CS60010)
Honor Code
Academic integrity is very important for us. You are required to follow the honor code to maintain academic integrity.
- Your solutions against assignments, tests must be entirely your own (Exception: You may collaborate if instructed by the faculty).
- You may not share your solutions for the scheduled assignments and tests with your peers unless instructed by the faculty.
