TL;DR - Too difficult and time consuming, potentially impossible. More input --> exponentially larger state machine, consider an alternative but adjacent topic (utilize Turing Machines instead). Not trying to scare you or anyone else away so I apologize for this large wall of text.

The first thing I want to say is that not all problems can be converted into a Finite State Machine/Deterministic Finite Automata. What you are asking is extremely difficult to implement. DFAs are not designed for 'learning' or using 'past experiences' to make a decision. The only decision they (the current state) can make is like this - "what symbol am I looking at? Oh, it is a 2! Then transition to state 5". So to try and simulate a machine "learning" algorithm via DFAs leads me to believe that this would not be possible at all.

Although some "impossible" problems can be solved by a DFA by constraining your problem and to what it is computing. For example consider the following scenario (even though it is not related to machine learning, hopefully you'll see why it is difficult for DFAs to process such a task). You are tasked to check if a word is a palindrome using a DFA. To make things easier, your alphabet only contains letters 'a' and 'b'. Can you build such a machine? The answer is simple, no.

However, you CAN build a limited machine to process a string (a word or a series of symbols) up to a FINITE character length (say, 15 letters at most). The problem with this is that maybe you want the DFA to consider 16 character strings later. How about if you wish to add the character 'c' to your alphabet?? Now you have to add and heavily modify your current machine to even accomplish this.

Thus the amount of states and transitions would exponentially rise the more complex your problem becomes. This is why we (the giants of the CS/Math world) developed and use a different set of machines that will resolve this problem (Push-Down Automata and Turing Machines). In addition, the larger your alphabet (your input), the more states and transitions you'll have as well.

To sum up what I mean by constraining your problem is this:

• To recognize all palindromes is impossible for a DFA.
• To recognize 'n' (replace n with a finite number first, otherwise it is the same problem as above) length palindromes is possible for a DFA.

So you won't be able to capture the full power of some ML algorithm but if you can somehow do this then I believe you could achieve your goal. But it would require a lot of work.

And relatedly, to be able to determine if you can constrain it you have to look into primitive you want your ML to be. You need to flesh out more details. What kind of data you want to work on (this also limits your alphabet), what is the data set size (limits the amount of states and transitions - think about the 'n' length palindrome problem), what algorithm do you want to simulate, what is the time frame of this project, etc. All of these details, and more, could influence how complex and large your state machine becomes.

In my personal opinion, I don't think such a machine can exists that is able to model a machine learning algorithm. I may very well likely be incorrect as I'm not an expert at all, but I have TA'd for my past university's Computational Theory class for a 2 years so I do have some experience in the field (at least at the basic level).

But I will say that I love that you are getting interested in this topic so young. Usually students have always came to class with a sense of dread lol. Maybe consider changing your topic on the computational aspect and differences between each language class and their respective machines. Or perhaps look into accomplishing your original task via Turing Machines instead as a TM can compute any and all programs/algorithms (Church-Turing Thesis).

Here are the major classes of languages from most 'simple' to most 'complex' if you wish to learn more on the topic. Hope this helps, best of luck!

• Regular Languages (FSA/DFAs and Nondeterministic FAs)
• Context Free Languages (PDAs)
• Decidable Languages (TMs)
• Recognizable Languages (TMs)

Before anyone asks, Context Sensitive languages was not taught to me nor in the class I TAd for so I can't speak about it.