1) Specify what cross-validation is used for and with the help of an example explain how it works.

2) For many machine learning problems, multiple valid hypotheses are possible. Specify the factors that generally determine which hypothesis is best out of the set of valid hypotheses.

3) Describe three methods that can be used to prevent decision trees from over-fitting the training data

4)Explain the concept of ensemble learning, specify the two key properties that the learners need to have in order for ensemble learning to be effective and expplain why the two key properties are necessary.

5) Incremental learning uses three types of memory models to store knowledge and information observed in the training data. Describe two memory models along with their advantages and disadvantages and explain which is best suited for real world applications.