Math 0?1 Matrix Calculus in Data Science & Machine Learning

Description

Descriptions

Math 0-1: Matrix Calculus in Data Science & Machine Learning, Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you’ll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices. Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal. In the first part of the course, we’ll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.

What you’ll learn

• Derive matrix and vector derivatives for linear and quadratic forms
• Solve common optimization problems (least squares, Gaussian, financial portfolio)
• Understand and implement Gradient Descent and Newton’s method
• Learn to use the Matrix Cookbook

Who this course is for

• Students and professionals interested in the math behind AI, data science and machine learning

Requirements

• Competence with Calculus and Linear Algebra
• Optional: Familiarity with Python, Numpy, and Matplotlib to implement optimization techniques