I am having an AI exam in two weeks, and I am still figuring out certain concepts and ideas, related to Bayesian Nets, Hidden Markov Chains, Conditional Random Fields and Neural Nets (yes it is all going to be tested and yes we have a text (AI - A Modern Approach), but no, we did not cover everything in class).
I "know" a few things about the mathematical descriptions of all of those, but I know pretty much nothing about their usage or practical applications.
Here are my questions (and I apologize for my naivety):
- What kind of machine learning algorithm classes do they belong to? Since they all need training, does it mean that they are all supervised learning algorithms?
- They all have an underlying structure that allows a graph to represent them, where directed edges denote dependencies between states. The probability of being in a state is computed as a conditional probability from ancestors of the state. Does that sound about right?
- In what kind of situation do you want to use which of the algorithms? Is it possible to some it up, or does it require subtle differentiation and expert-level knowledge?
- Why do Neural Nets get special treatment? I heard of many classes teaching Neural Nets, but I have heard of no such thing for the other guys.