Calculus for Machine Learning

[Mustjaab]

Description

A course walking through critical points, derivatives, and integrals within the context of machine learning - both applications of models, and how.calculus can be used to enhance performance of models themselves.

[Haleshot]

Ah nice; can see this leading to a good series of notebooks. Have any official, credible resources that you can link which can help in forming a basic, initial outline for this course?

[Mustjaab]

For sure! In terms of just calculus, I think we can use refer from Khan Academy - it's pretty hollistic in terms of covering calculus topics. https://www.khanacademy.org/math/calculus-1. In terms of bridging calculus and ML, we can use this resource: https://www.youtube.com/playlist?list=PLRDl2inPrWQVu2OvnTvtkRpJ-wz-URMJx.

[Haleshot]

Nice resources (especially the second link). For calculus specific topics, I believe we can follow a mix of khan academy and the YT playlist's respective video for the topic. Overall, I think the YT playlist is more relevant and provides a good outline/structure to follow in terms of flow for the series of notebooks.

Would you be open to creating/modifying the above issue contents appropriately? We've established templates for outlining the notebooks (tentative) in issues liek #48 #40 , etc.

[Mustjaab]

Calculus for Machine Learning

Claiming a notebook.

Any notebook without an assigned author needs a contributor. To get started, leave a comment to claim a notebook you'd like to contribute, and then create a pull request with your draft.

Notebook	Description	Status	Author
Rates of Change	Introduction to average rate of change and instantaneous rate of change	🚧	@k-anisha
Derivatives and Critical Values	Transition from instantaneous rate of change to finding maximum and minimum value of functions	🚧	@ghimiresunil
Optimization	Utilize the cvxpy to learn how critical values can be used to optimize functions	🚧
Antiderivatives and Integrals	Both business and science applications of integrals as well as within machine learning	🚧
Partial and Second Order Derivatives	Introduction to Jacobian and Hessian matrices	🚧
Calculus for Statistics	Use of calculus in stochastic systems	🚧
Neural Networks	Overview of using functions like sigmoid, rectified linear unit, tangent, and others	🚧

[Haleshot]

Hey @Mustjaab, noticed the table didn't render — probably a markdown syntax issue. Also curious about your thoughts on the course structure whenever you have time!

[Haleshot]

Came across a useful (relevant) resource in regard to the above course: https://arxiv.org/abs/2501.14787

[Mustjaab]

Definitely, I especially like the Calculus for Variations.

In terms of structure, I'm thinking some of the notebooks could invite users to submit something. For example, their own application integrals or something like that.

[k-anisha]

Hey @Haleshot, can I work on Rates of Change?

[Haleshot]

Hey @Haleshot, can I work on Rates of Change?

Sure! Assigned it above. Let me know if you need any help with coming up w/ content for the topic/notebook.