UNIT-I QUESTIONS
1. How a model will learn from a class of example? Explain with example.
2. Define VC dimension. Show that an axis aligned rectangle can scatter 4 points in 2 dimensions.
3. How VC dimension is related with no of training examples used for learning?
4. Give a Brief note on PAC learning.
5. How a model will learn from multiple classes? Explain with example.
6. What is regression? Write about linear and multiple regression algorithms
7. Write about different parameters need to consider while selecting and generalizing a model.
8. What are the Dimensions of supervised Machine learning algorithm?
9. Explain the concept of Bayes theory with an example.
UNIT-II QUESTIONS
1. Explain Maximum Likelihood Estimation with examples.
2. How to evaluate an estimator using bias and variance? Explain.
3. Explain in detail about Bayes' Estimator.
4. How Parametric Classification is done? Explain.
5. Explain in detail about Tuning Model Complexity.
6. Write a brief note on various Model Selection Procedures.
UNIT-III QUESTIONS
1. Write about subset selection in Dimensionality Reduction.
2. Explain Principal Components Analysis with example.
3. Write about Factor Analysis in detail.
4. Explain Linear Discriminant Analysis with example.
5. Explain Apriori Algorithm with example.
6. Explain FP Growth Algorithm with example.
UNIT-IV QUESTIONS
1. With an example, explain EM Algorithm.
2. What are Self-Organizing Maps(SOM)? Explain SOM learning process with algorithm.
3. What are advantages of ART? Explain ART versions and use cases.
4. With an example, explain K-means clustering algorithm.
5. With an example, explain documentation clustering algorithm.
6. With an example, explain Spectral clustering algorithm.
7. With an example, explain Hierarchical Clustering algorithm.
8. Write about a) Supervised Learning after Clustering b) Choosing the Number of Clusters
UNIT-V QUESTIONS
1. Write about Univariate Trees
2. How to avoid overfitting in decision trees? Explain.
3. With an example, explain how to extract rules from decision tree.
4. Write the process of learning rules from data.
5. Explain random forest algorithm with an example.
6. Write the differences between decision tree and random forest.
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