[jules]

Most of ML is fitting models to data. To fit a model you minimise some error measure as a function of its real valued parameters, e.g. the weights of the connections in a neural network. The algorithms to do the minimisation are based on gradient descent, which depends on derivatives, i.e. differential calculus.

If you're doing Bayesian inference you're going to need integral calculus because Bayes' law gives the posterior distribution as an integral.

For ML you just need Calculus 1 and 2. The curl/div and Stokes is Calculus 3 which a physics thing. You don't need that for ML.

You may need the basics of functional analysis in certain areas of ML, which is arguably Calculus 4.

[LrnByTeach]

Could not agree more .......

> Most of ML is fitting models to data. To fit a model you minimise some error measure as a function of its real valued parameters, e.g. the weights of the connections in a neural network. The algorithms to do the minimisation are based on gradient descent, which depends on derivatives, i.e. differential calculus.

> If you're doing Bayesian inference you're going to need integral calculus because Bayes' law gives the posterior distribution as an integral.