Assignments

1 Introduction

The assignments documented here reflect a comprehensive study of the concepts and tools taught in the course Computational Mathematics for Data Science. These include:

1.1 Overview

  1. Linear Algebra Fundamentals:

    • Matrix operations, four fundamental subspaces, Matrix decompositions- CR, LU, QR, Spectral , SVD and PCA.
    • Solving systems of linear equations.
    • Applications in Machine Learning.

  2. Advanced Topics in Optimization:

    • Convex optimization and its applications.
    • Lagrange methods for optimization.
    • Applications in computational data science.

2 Structure

Each of the 83 assignments follows a consistent structure:

  • Conceptual Summary: A concise review of the topic’s theoretical framework.

  • Problem Solving: Mathematical derivations and computational implementation using MATLAB.

  • Optimization Techniques: For relevant topics, problems were solved using the CVX solver.

For convenience and continuity, each set of assignments is embedded for easy access:

2.1 Assignment Set 1

This set contains assignment 1 to 10.

2.2 Assignment Set 2

This set contains assignment 11 to 20.

2.3 Assignment Set 3

This set contains assignment 21 to 30.

2.4 Assignment Set 4

This set contains assignment 31 to 48.

2.5 Assignment Set 5

This set contains assignment 49 to 64.

2.6 Assignment Set 6

This set contains assignment 65 to 79.

2.7 Assignment Set 7

This set contains assignment 80 to 83.