What are overfitting and underfitting in machine learning?

• Discuss their causes and how to address them (e.g., regularization).

What is the difference between classification and regression?

• Provide examples of each.

What are the different types of activation functions in neural networks?

• Explain ReLU, Sigmoid, and Tanh with their pros and cons.

How does gradient descent work in training a model?

• Mention optimization and learning rate adjustments.

What is reinforcement learning? Can you give an example?

• Explain the concept with real-life scenarios like game-playing agents.

What are decision trees and random forests?

• Describe their structure and when to use them.

How is bias different from variance in machine learning models?

• Discuss their trade-offs in the context of model performance.

What is the role of a confusion matrix in evaluating AI models?

• Define terms like precision, recall, F1 score, and accuracy.

What is a convolutional neural network (CNN)?

• Discuss its architecture and usage in image processing.

What is transfer learning, and why is it important?

• Provide examples like using pre-trained models (e.g., ResNet, BERT).