Welcome to the repository for the Computational Mathematics for Data Science course. This repository contains all the assignments and projects completed as part of the coursework. The course covers essential topics in linear algebra, optimization, and their applications in machine learning. Each assignment and project is designed to help reinforce the key concepts learned throughout the course.
This course introduces fundamental and advanced topics in linear algebra and optimization, with a focus on their applications in data science and machine learning. Key concepts such as matrix operations, eigenvalue decomposition, Singular Value Decomposition (SVD), and optimization algorithms are explored. These techniques are then applied in machine learning projects that involve real-world data analysis and model development.
The assignments cover a wide range of topics and are solved both mathematically and computationally using MATLAB. Each assignment is designed to build a strong foundation in linear algebra and optimization techniques.
You can find the assignments in the Assignments here:
- Assignment Set 1: 1-10
- Assignment Set 2: 11-20
- Assignment Set 3: 21-30
- Assignment Set 4: 31-48
- Assignment Set 5: 49-64
- Assignment Set 6: 65-79
- Assignment Set 7: 80-83
For a complete list of all assignments, kindly check the Assignments and projects folder in the repository.
The projects focus on applying the concepts learned in the course to real-world problems. These projects incorporate both statistical methods and machine learning algorithms to solve practical problems.
You can find the projects in the Projects here:
-
Project 1: Classification of Benign and Malignant Tissue
-
This project involves using both statistical and machine learning algorithms to classify benign and malignant tissue samples. The CVX solver is used for optimization tasks within the project.
-
Presentation is available at Presentation: Project 1
-
-
Project 2: SVD Based Image Processing Applications
-
This project explores the use of Singular Value Decomposition (SVD) for three important image processing techniques: image compression, denoising, and forensics. It is a partial independent replication of Sadek's work in the field.
-
Presentation is available at Presentation: Project 2
-
A digital version of this project is available at: https://sijuswamy.github.io/SVD_project/.
- MATLAB: Used for solving optimization problems with the CVX solver.
- CVX Solver: A MATLAB-based optimization solver used for modeling and solving convex optimization problems.
- Python: Used for SVD project.
This repository is licensed under the MIT License.
I would like to express my sincere gratitude to the following individuals for their invaluable guidance and support throughout the Computational Mathematics for Data Science course and during the completion of this project:
- Dr. K.P. Soman, Dean, ASAI: For his continuous encouragement, guidance, and support throughout the course.
- Dr. Vipin V, Thesis Supervisor: For his mentorship, insightful feedback, and unwavering support in the successful completion of my coursework and projects.
- Dr. Soumya V, Coordinator, PG Programmes: For her constant motivation, constructive feedback, and organization of the course activities.
- The faculties of the Department of Computational Engineering and Networking (CEN) at Amrita Vishwa Vidyapeetham, Coimbatore: For their support and dedication to providing quality education in computational mathematics and machine learning.
Thank you all for helping me develop the skills and knowledge that have been essential to my academic and professional growth.
Thank you for visiting Course work repository. Please feel free to explore the assignments and projects, and don't hesitate to reach out if you have any questions or suggestions at [email protected]!