There of course are many, but for someone coming from computer science, and, software engineering, where the environment is relatively clean and certain (deterministic), it usually is a leap to understand that Machine Learning (and other elements of #AI) are not.
Machine learning, is based on probability theory and deals with stochastic (non-deterministic) elements all the time. Nearly all activities in machine learning, require the ability to factor and more importantly, represent and reason with uncertainty.
To that end, when designing a system, it is recommended to use a simple but uncertain (with some non-deterministic aspects) rule, rather than a complex but certain rule.
For example, having a simple but uncertain rule saying “most birds fly”, is easier and more effective than a certain rule such as “Birds can fly, except flightless species, or those who are sick, or babies, etc.”
As one starts getting deeper in Machine Learning, a trip down memory lane around Probability distribution , expectation, variance , and covariance won’t hurt.