What is an artificial neural network and for what types of problems can it be used? $2.$ Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar? $3.$ What are the most common ANN architectures? For what types of problems can they be used? $4.$ ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. $5.$ What are SVM$?$ How do they work? $6.$ What are the types of problems that can be solved by SVM$?7.$ What is the meaning of $$maximum$-$margin hyperplanes$?$ Why are they important in SVM$?8.$ What is the kernel trick and how does it relate to SVM$?9.$ What are the specific steps to follow in developing an SVM model? $10.$ How can the optimal kernel type and kernel parameters be determined? $11.$ What are the common application areas for SVM$?$ Conduct a search on the Internet to identify popular application areas and specific SVM software tools used in those applications. $12.$ What are the commonalities and differences, advantaged and disadvantages between ANN and SVM$?13.$ Explain the difference between a training and a testing data set in ANN and SVM$.$ Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not? $14.$ Everyone would like to make a great deal of money on the stock market. Only a few are very successful. Why is using an SVM or ANN a promising approach? What can they do that other decision support technologies cannot do$?$ How could SVM or ANN fail? $15.$ What is special about the kNN algorithm? $16.$ What are the advantages and disadvantages of kNN as compared to ANN and SVM$?17.$ What are the critical success factors for a kNN implementation? $18.$ What is a similarity $($or distance$)$ measure? How can it be applied to both numerical and nominal valued variables? $19.$ What are the common $($business and scientific$)$ applications of kNN$?$ Conduct a Web search to find three realworld applications that use kNN to solve the problem. $20.$ What is special about the Na$$ve Bayes algorithm? What is the meaning of $$Na$$ve$$ in this algorithm? $21.$ What are the advantages and disadvantages of Na$$ve Bayes compared to other machine$-$learning methods? $22.$ What type of data can be used in a Na$$ve Bayes algorithm? What type of predictions can be obtained from it$?23.$ What is the process of developing and testing a Na$$ve Bayes classifier? $24.$ What are Bayesian networks? What is special about them? $25.$ What is the relationship between Na$$ve Bayes and Bayesian networks? $26.$ What is the process of developing a Bayesian networks model? $27.$ What are the advantages and disadvantages of Bayesian networks compared to other machine$-$learning methods? $28.$ What is Tree Augmented Na$$ve $($TAN$)$ Bayes and how does it relate to Bayesian networks? $29.$ What is a model ensemble, and analytically where can it be used? $30.$ What are the different types of model ensembles? $31.$ Why are ensembles gaining popularity over all other machine$-$learning trends? $32.$ What is the difference between bagging$-$ and boostingtype ensemble models? $33.$ What are the advantages and disadvantages of ensemble models