Prompt Title: MLDL

Here are some questions that might be asked in a viva for Machine Learning:

1. What is Machine Learning, and how does it differ from traditional programming techniques?
2. Explain the types of Machine Learning algorithms and their applications.
3. What are the most commonly used performance metrics in Machine Learning?
4. What is overfitting, and how can it be avoided?
5. Describe the bias-variance tradeoff in Machine Learning.
6. What is regularization, and how does it help to prevent overfitting?
7. What is cross-validation, and why is it important in Machine Learning?
8. How can missing data be handled in Machine Learning?
9. What are the different techniques used for feature selection in Machine Learning?
10. What is clustering, and what are some popular clustering algorithms?
11. What is reinforcement learning, and how is it different from supervised and unsupervised learning?
12. Explain the working of decision trees and their applications.
13. What is deep learning, and how is it different from traditional Machine Learning?
14. Explain the backpropagation algorithm and how it is used in neural networks.
15. What are convolutional neural networks, and what are their applications?

These are just a few examples, and the specific questions asked may vary depending on the focus of the viva and the level of the candidate's expertise.