Matrixcalc

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matrixcalc is an open-source repository for an MIT IAP short course on Matrix Calculus for Machine Learning and Beyond. It provides comprehensive course materials, lecture notes, and problem sets for advanced mathematical concepts.

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Open Source

Free tier

Yes

API

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Technical

About

What is matrixcalc?

matrixcalc is an open-source GitHub repository hosting the materials for the MIT IAP short course, "Matrix Calculus for Machine Learning and Beyond." Taught by Professors Alan Edelman and Steven G. Johnson, this resource extends traditional calculus to matrix functions and arbitrary vector spaces, crucial for modern applications like machine learning and large-scale optimization. It covers topics such as derivatives as linear operators, multidimensional chain rules, automatic differentiation, and adjoint methods. The course emphasizes matrices as holistic objects and includes practical aspects like numerical computations using the Julia language, making it a valuable resource for those looking to deepen their understanding of advanced calculus in a computational context.

Best used for

Ideal for professors and teachers who need to deliver advanced courses on matrix calculus for machine learning and optimization, and for students seeking in-depth learning materials. Especially valuable for those who want to understand the theoretical underpinnings and practical applications of derivatives in complex computational systems.

Common actions

teach matrix calculus

learn machine learning math

understand automatic differentiation

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Capabilities

Key features

Comprehensive lecture notes
Problem sets
Julia language integration
Automatic differentiation concepts
Matrix factorization derivatives

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What topics are covered in the matrixcalc course materials?

The course covers derivatives as linear operators, matrix functions, Kronecker products, matrix factorizations, multidimensional chain rules, forward and reverse-mode automatic differentiation, adjoint methods, and applications in optimization and machine learning.

Does the course involve practical computational work?

Yes, the course involves simple numerical computations using the Julia language. Instructions for installing Julia are provided, along with a fallback option to run it in the cloud, making it accessible for practical exercises.

Are there problem sets and exams for the course?

The course includes two problem sets that are submitted electronically via Canvas. There are no exams, focusing instead on practical application and understanding through the problem sets.

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