Projects
1 Introduction
This section showcases the two major projects completed as part of the Computational Mathematics for Data Science course. These projects demonstrate the application of concepts and tools learned during the coursework.
2 Project I: Benign and Malignant Tissue Classification
2.1 Overview
Objective: Classify benign and malignant tissue using statistical methods and classical machine learning algorithms.
Tools: MATLAB and CVX solver for implementing predictive and prescriptive models.
Highlights:
- A statistical approach for feature selection.
- Development of a predictive model using supervised learning.
- Formulation of a prescriptive model using unsupervised learning methods.
Presentation of the project is available at:
3 Project II: Applications of Singular Value Decomposition (SVD)
3.1 Overview
Objective: Explore the theoretical and practical utility of SVD in digital image processing.
Techniques:
- Image Compression: Reducing image size while preserving quality.
- Denoising: Enhancing image clarity by removing noise.
- Forensic Analysis: Extracting and analyzing hidden image details.
Reference: A partial replication of Sadek’s work, extending its applications.
Tools: MATLAB implementations and theoretical discussions.
Presentation of the project is available at:
A digital version of this project is available at Project-2