What is the purpose of modelling in AI?

To create a simplified representation of a complex real-world system.

What is a neural network?

A computational model inspired by the structure and functioning of biological brains.

A programming language used in AI.

What is the purpose of training a machine learning model?

To enable the model to make accurate predictions or classifications based on the given data.

To create a simplified representation of a complex real-world system.

To optimize the performance of the model.

What is reinforcement learning?

A type of machine learning where an agent learns through trial and error by interacting with an environment.

A technique for visualizing data in AI models.

A way to optimize the performance of machine learning algorithms.

A method for training neural networks.

What is a supervised learning model?

A model that learns patterns from labeled training data.

A model that learns patterns from testing data.

A model that learns patterns from unlabeled training data.

A model that learns patterns from both labeled and unlabeled training data.

In a neural network, what is the role of activation functions?

To introduce non-linearity in the output

To define the number of hidden layers

In a neural network, what is the purpose of the training phase?

To optimize the model's parameters

To evaluate the model's performance

What is AI modelling?

The process of creating a mathematical representation of an AI system's behavior or decision-making process

The process of collecting data for an AI system

The process of training an AI system

The process of validating an AI system

The process of deploying an AI system

What is the role of evaluation in AI modelling?

To assess the performance of the model and identify areas for improvement

What is the goal of AI modelling?

To create an accurate and effective representation of an AI system

To optimize the model's complexity

To validate the model's performance

What is overfitting in AI modeling?

When a model performs well on training data but fails to generalize to new, unseen data.

When a model performs equally well on both training and testing data.

When a model fails to learn patterns in the training data.

When a model is unable to learn from the training data.

What is unsupervised learning?

A machine learning technique where the model learns from unlabeled data.

A machine learning technique where the model learns from labeled data.

A machine learning technique used only for classification problems.

A machine learning technique where the model learns without any training data.

What is the purpose of cross-validation in AI modeling?

To assess the performance and generalization ability of a model on unseen data.

To randomly split the training and testing data.

To prevent overfitting by reducing the training data size.

To increase the model complexity and capacity.

What is overfitting in AI?

When a machine learning model performs well on the training data but fails to generalize on new, unseen data.

When a machine learning model is not complex enough to capture the underlying patterns in the training data.

When a machine learning model perfectly generalizes to new, unseen data.

When a machine learning model fails to learn from the training data.

What is underfitting in AI?

When a machine learning model is too simple to capture the underlying patterns in the training data.

When a machine learning model perfectly generalizes to new, unseen data.

When a machine learning model performs well on the training data but fails to generalize on new, unseen data.

When a machine learning model is not able to learn from the training data.

What is one way to address underfitting in AI?

Increasing model complexity or adding more features

What is the main consequence of overfitting in AI?

Poor generalization to new, unseen data

Accurate predictions on the training data

Improved performance on new, unseen data

Better understanding of the underlying patterns

What is the main difference between overfitting and underfitting in AI?

Overfitting occurs when the model is too complex and fits the training data too closely, while underfitting occurs when the model is too simple and fails to capture the underlying patterns in the data.

Overfitting occurs when the model performs well on both the training and test data, while underfitting occurs when it performs poorly on both.

Overfitting occurs when the model is too simple and fails to learn from the training data, while underfitting occurs when the model is too complex and memorizes the training data.