Here are some questions that might be asked in a viva for Machine Learning:
- What is Machine Learning, and how does it differ from traditional programming techniques?
- Explain the types of Machine Learning algorithms and their applications.
- What are the most commonly used performance metrics in Machine Learning?
- What is overfitting, and how can it be avoided?
- Describe the bias-variance tradeoff in Machine Learning.
- What is regularization, and how does it help to prevent overfitting?
- What is cross-validation, and why is it important in Machine Learning?
- How can missing data be handled in Machine Learning?
- What are the different techniques used for feature selection in Machine Learning?
- What is clustering, and what are some popular clustering algorithms?
- What is reinforcement learning, and how is it different from supervised and unsupervised learning?
- Explain the working of decision trees and their applications.
- What is deep learning, and how is it different from traditional Machine Learning?
- Explain the backpropagation algorithm and how it is used in neural networks.
- 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.