Understanding AI Modelling

What is AI modelling?

• AI modelling refers to the creation of physical robots for automation.

• AI modelling is the process of designing user interfaces for applications.

• AI modelling is the process of creating algorithms and models that allow machines to learn from data and make predictions.

• AI modelling is the study of human behavior in social settings.

Name two types of AI models.

• Supervised learning models, Unsupervised learning models

• Generative models

• Reinforcement learning models

• Deep learning models

What is the purpose of training an AI model?

• To create a user interface for the AI model.

• To store data without processing it.

• The purpose of training an AI model is to enable it to learn from data and improve its performance on specific tasks.

• To generate random outputs without learning.

Explain the term 'dataset' in AI modelling.

• A dataset is a type of AI algorithm used for predictions.

• A dataset is a structured collection of data used for training and testing AI models.

• A dataset is a single data point used in AI analysis.

• A dataset is a random collection of unrelated data points.

What is overfitting in machine learning?

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting happens when a model is trained on too much data, leading to confusion.

• Overfitting is when a model performs poorly on both training and new data due to lack of data.

How does supervised learning differ from unsupervised learning?

• Supervised learning uses only numerical data, while unsupervised learning uses categorical data.

• Unsupervised learning requires labeled data, while supervised learning works with unlabeled data.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning requires labeled data, while unsupervised learning works with unlabeled data.

What role does data preprocessing play in AI modelling?

• Data preprocessing eliminates the need for data analysis in AI modeling.

• Data preprocessing is only necessary for supervised learning models.

• Data preprocessing enhances data quality and prepares it for effective AI modeling.

• Data preprocessing reduces the amount of data available for AI modeling.

Define the term 'algorithm' in the context of AI.

• An algorithm is a defined set of rules or instructions for solving a problem or performing a task in AI.

• A method for storing data in AI systems.

• A type of programming language used in AI.

• A hardware component essential for AI operations.

What is the significance of validation in AI modelling?

• Validation ensures model accuracy, prevents overfitting, and assesses generalization.

• Validation increases model complexity

• Validation is only necessary for supervised learning

• Validation has no impact on model performance

Give an example of a real-world application of AI modelling.

• AI in video game graphics rendering

• AI for automated email sorting

• Predictive analytics in healthcare for patient outcome forecasting.

• AI in social media content moderation