Previous Projects

Project MIV2

This project launched by Dr. Sachin Pathak is an industry-linked model that attempts to deliver business solutions by integrating concepts of Math with concepts in Computer Science. This year, 1st Year BTech students who learn Linear Algebra and Differential Equations get to explore how these concepts are used in Digital Image Processing. Students work on various image processing related activities like- Face Detection,  Background and Object Detection, Face Verification and Cropping and Fashion Detection.Mentors from the Industry help the students to use various AI applications that are used to train programs. Through these activities students will receive hands-on exposure and  will work on real world scenarios that are prevalent in the industry. 


The learnings from these interrelated activities are summarized by the Students as part of their project reports. It covers the implementation of linear algebra in image processing, image processing concepts and understanding the framework behind training a machine learning model.


This project spans 6 to 8 weeks and includes periodic team reviews by Mentors and CoEET, mid project reports and final presentation to a panel of Professors. The mid-project carries 5 marks and the final evaluation - 10 marks. 

MIV2 Project Guidelines.pdf
MIV2 - Roles and Responsibilities V2.0.pdf
NU MIV2 Project Curriculum - Google Docs.pdf
Maths CVIP Timeline Chart
MIV2 teams with Mentors.xlsx
Inputs from the Industry Mentors - 1.pdf
Industry Inputs to MIV2.pdf

Project Math/ Programming

The Mathematics in Machine Learning project series integrates two crucial subject matters in our course, Mathematics and Computer Science. These two subjects, when intertwined, leading to the emergence of technology that is rooted in the theories of Mathematics and implemented with the reinforcements of Computer Science, in the real world. Just as the NanoEPL (LPL), the Math in ML project is assigned to the Freshmen, who are assisted and mentored by Sophomores and Associate Mentors from the Junior and Senior years. The series extends over a period of two months and the evaluation component comprises progress reports, weekly CoEET evaluation, video logs, and a final presentation before the faculty.


RESOURCES

mlteam-presentation.pptx