Opportunity brief
Machine Learning is becoming an essential skill for engineers and is now a key component of major hackathons and industry projects. ML Bubble provides students with an opportunity to explore real-world problems and develop ML-based solutions according to their academic level.
The event is divided into three tracks:
FE – Explore & Identify
- Identify a real-world problem.
- Explain why Machine Learning can help solve it.
- Submit a short presentation or write-up.
SE – Design & Solve
- Design an ML-based solution.
- Train a working model.
- Present results and evaluation metrics.
- Submit PPT and model.
TE-BE – Design & Solve (Advanced)
- Build and train a working ML model.
- Present results and performance metrics.
- Include comparative analysis and deployment considerations.
- Submit PPT and model.
Suggested Problem Domains
Participants may choose problem statements from, but are not limited to, the following domains:
- Healthcare & Medical Technology
- Agriculture & Smart Farming
- Defense & National Security
- Cybersecurity
- Finance & FinTech
- Education Technology (EdTech)
- Smart Cities & Urban Development
- Environment & Sustainability
- Industrial Automation & Manufacturing
- Transportation & Logistics
- E-Commerce & Retail Analytics
- Human Resources & Recruitment
- Social Impact & Public Welfare
- Energy & Power Management
- Sports Analytics
- Media & Entertainment
- Natural Language Processing (NLP)
- Computer Vision & Image Processing
- Internet of Things (IoT) & Smart Systems
- Predictive Analytics & Decision Support Systems
Note
Participants are free to choose any domain, provided that the proposed solution involves a significant Machine Learning component and demonstrates its practical application to solve a real-world problem.