Most of the algorithms in machine learning I am aware of use datasets and learning happens in an iterative manner given some examples. The examples can also be understood as experience in the case of reinforcement learning.
Consider the following from Numerical Computation chapter of Deep Learning book
Machine learning algorithms usually require a high amount of numerical computation. This typically refers to algorithms that solve mathematical problems by methods that update estimates of the solution via an iterative process, rather than analytically deriving a formula to provide a symbolic expression for the correct solution. Common operations include optimization (ﬁnding the value of an argument that minimizes or maximizes a function) and solving systems of linear equations. Even just evaluating a mathematical function on a digital computer can be diﬃcult when the function involves real numbers, which cannot be represented precisely using a ﬁnite amount of memory.
I am wondering whether there is any domain in machine learning that deals with solving the problem analytically rather than computationally heavy iterative algorithms?