Q: Can we say that all of machine learning is, essentially, only about finding good estimations of programs? If not, is there any example of a machine learning problem that is not about finding estimations of programs?
Examples of programs could be simple decision problem solvers, such as binary classification, regression, clustering, or more complicated programs such as those that generate special sequences of output (e.g. instructions for a self-drive automotive, human text, audio signals, etc).
Some definitions in case it helps clarifying the context.
A program is defined here as follows:
A computer program is a collection of instructions that performs a specific task when executed by a computer. A computer requires programs to function, and typically executes the program's instructions in a central processing unit.
Machine learning is defined here as follows:
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.