Welcome to my repository for the Master's in Computational and Applied Mathematics program at the University of Chicago! This repository contains a collection of my homework assignments, lecture notes, and code developed during my time in the program.
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Homework: This directory contains all of the homework assignments I completed throughout the program. Each subdirectory corresponds to a different course, and within each subdirectory, you'll find the assignments organized by assignment number.
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Lecture Notes: In this directory, you'll find my comprehensive notes from various lectures attended during the program. These notes cover a wide range of topics related to computational and applied mathematics, providing valuable insights and references for further study.
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Code: Here lies the code I developed as part of my coursework and personal projects. The code is organized into directories based on the relevant courses or projects. Each directory contains the source code, along with any necessary documentation or supplementary materials.
Throughout the program, I enrolled in a variety of courses covering diverse aspects of computational and applied mathematics. Some of the key courses included:
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Numerical Methods: This course delved into the numerical techniques used to solve mathematical problems, including numerical linear algebra, optimization methods, and numerical integration.
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Advanced Differential Equations: Explored advanced topics in differential equations, including partial differential equations, boundary value problems, and applications in physics and engineering.
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Probability and Statistics: Covered fundamental concepts in probability theory and statistical analysis, with applications in data science, machine learning, and financial modeling.
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Optimization: Focused on optimization theory and algorithms, including linear programming, nonlinear optimization, and convex optimization.
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Machine Learning: Explored the theory and applications of machine learning algorithms, including supervised and unsupervised learning, neural networks, and deep learning.
To explore the contents of this repository, simply clone it to your local machine using the following command:
git clone https://github.com/CalebDerrickson/GradCourses.gitOnce cloned, feel free to navigate through the directories and files to access the homework assignments, lecture notes, and code samples. You may also contribute to this repository by submitting pull requests or opening issues for any suggestions or improvements.
If you have any questions or would like to get in touch, feel free to reach out to me via email at [email protected]