Machine Learning, BCA 8^th Semester,

Chapter 1

1. Difference between training data and testing data.
2. Differentiate between supervised , unsupervised and reinforcement learning.
3. What are issues in Machine Learning?
4. What do you mean by concept of learning?
5. What is overfitting? How can you avoid it?
6. What is training set and test set in ML model? How much data will be allocated for training, validation and test set?
7. What are the applications of ML in real time?
8. What is meant by semi-supervised learning?
9. What is Bias and Variance in a Machine Learning model?
10. What is Trade-off Between Bias and Variance.

Chapter 2

1. Greedily learn a decision tree using ID3 algorithm and draw the tree.
2. Discuss Entropy in ID3 algorithm with an example.
3. Compare Entropy and Information gain in ID3 with an example.
4. Discuss Inductive Bias with respect to Decision Tree Learning.
5. What type of problem are best suited for decision tree learning.
6. What are the steps of ID3 algorithm. Explain the capabilities and limitation of ID3.
7. What is Support Vector Machine? Discuss in detail.
8. Explain the concept of linear regression.
9. What are problem of Decision Tree learning.
10. Explain the concept of Entropy and Information gain.
11. What Is meant by deterministic algorithm?
12. How do you design an email spam filter?
13. What is Kernal SVM?
14. Compare classification and regression.

Please also practice the numerical problems we learned in class.

Chapter 3

1. Explain the concept of Bayes Theorem with an example.
2. Explain Bayesian Belief Network and conditional independence with example.
3. What are Bayesian Belief Network? Where are they used?
4. Explain the K- Nearest Neighbour algorithm with neat diagram.
5. What is ‘naïve’ in the ‘naïve bayes’ classifier?
6. What is Support vector in SVM?

Chapter 4

1. What  is Clustering? Explain K-mean algorithm with an example.
2. Define
3. Prior Probability
4. Conditional Probability
5. Posterior Probability
6. Explain the concept of Naïve Bayes Classifier with an example.
7. What is reinforcement learning?
8. Define clustering. What are the different types of clustering? Explain in detail.
9. Explain PCA and its process with their applications.
10. Explain detail concept of K-mean.
11. Compare K-mean and KNN algorithm.
12. What are some common methods of reducing dimensionality?
13. What is the difference between Entropy and Information Gain.

Chapter 5

1. What is confusion matrix in Machine Learning?
2. Calculate accuracy, TPR, FPR and precision for given confusion matrix for a classifier
3. What is FP and FN and How are they significant?
4. Define precision and Recall.
5. What do you mean by Type I and Type II errors?
6. Explain FN, FP, TN, TP with a simple example.
7.  What is ROC curve? What does ROC represent?