Q1

1.In 3-4 sentences compare artificial and biological neural networks. What aspects of biological neural networks are not mimicked by artificial ones? What aspects are similar?

2. 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?

Q2

1.In 3-4 sentences describe what are the applications of KNN search and when we should not use this algorithm?

2.Discuss a real world example of KNN algorithm.

Q3

1. What is an artificial neural network and for what types of problems can it be used?
2. 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?
3. What are SVM? How do they work?
4. What are the types of problems that can be solved by SVM?
5. Why are support vector machines a popular machine-learning technique?
6. Why have neural networks shown much promise in many forecasting and business classification applications?
7. What does the Kohonen’s self- organizing feature map allow to be represented?
8. How is a general Hopfield network represented architecturally?
9. Describe the Tree Augmented Na¥ (TAN) Bayes method.
10. Describe the taxonomy for model ensembles.
11. List the pros and cons of model ensembles compared to individual models.
12. What are the three steps in the process-based approach to the use of support vector machines (SVMs)?
13. Describe the k-nearest neighbor (kNN) data mining algorithm?
15. Using Microsoft Excel, create a visual representation of the following:a.waffle chartb. bar chartc.Icon Array d. Donut Graph