**Machine Learning**, **BCA 8 ^{th} Semester,**

__Chapter 1__

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

__Chapter 2__

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

**Please also practice the numerical problems we learned in class.**

__Chapter 3__

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

__Chapter 4__

- What is Clustering? Explain K-mean algorithm with an example.
- Define
- Prior Probability
- Conditional Probability
- Posterior Probability
- Explain the concept of Naïve Bayes Classifier with an example.
- What is reinforcement learning?
- Define clustering. What are the different types of clustering? Explain in detail.
- Explain PCA and its process with their applications.
- Explain detail concept of K-mean.
- Compare K-mean and KNN algorithm.
- What are some common methods of reducing dimensionality?
- What is the difference between Entropy and Information Gain.

__Chapter 5__

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