Re:Ask HN: What maths are critical to pursuing ML/AI?
by LrnByTeach on Monday August 28, 2017 @05:03PM
(#15120049)
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.
> 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.