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

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.