10 development questions ( artificial intelligence and machine learning)

Answer these 10 questions (15 - 25 lines) 1- describe the common features of the following paradigms: RNA, SVM, Markov Models and Fuzzy Logic. 2- Is it necessary to prepare the data before treatment by one of the paradigms of question 1? if so, why do we do it? Discuss an example. 3- is there a relationship between RNA and SVM? if so, can we say that one of the concepts includes the other? 4- What does deep learning bring to connectionist systems? how can it be applied to recurrent neural networks? 5- What a fuzzy expert system brings that is not found in a traditional expert system? 6- describe the fundamental difference between a Poisson process and a Markov process; distinguish them in terms of applications. 7- mention the various problems that can be solved with a HMM and explain why the implementation of the latter would be unachievable without recourse to iterative methods. 8- explain the difference between a Markovian process and a Bayesian process? Give an example of application for both. 9- Imagine that we want to hybridize one of the paradigms of question 1 with a genetic algorithm, what could it be used for? Give an example by specifying the algorithm and the parameters involved. 10- compare the learning algorithm, Ant colony algorithms , to a neural network.