Project and Poster Competition

In the final component of this course, students will complete group project that applies machine learning techniques to a real-world problem within their own domain of interest, such as business, engineering, health, social sciences, or any other relevant field. Group will have 4 to 5 students strictly.

Students are expected to:

• Clearly define a problem and dataset relevant to their domain.
• Apply and compare multiple machine learning techniques covered in the course.
• Justify model choices and preprocessing decisions.
• Demonstrate measurable improvement in model performance through iterative refinement, feature engineering, or model tuning.
• Critically analyse results using appropriate evaluation metrics.

Each project proposal must be submitted for instructor approval before full implementation. Only approved projects may proceed to the final stage.

Upon successful completion of the project, students are required to design and present an academic-style poster that clearly communicates:

• The problem statement and motivation.
• The data and methods used.
• Model comparisons and performance improvements.
• Key insights, limitations, and conclusions.

The poster presentation serves as the final assessment of the project component and is mandatory for course completion.

Timeline:

Submit Project proposal (due date: 24 Apr 2026)
Fully implemented Proejct with results submission (due date: 15 May 2026)
Poster Submission (due date: 29 May 2026)
Date for poster presntation will be announced later.