Introduction to Artificial Intelligence with Focus on Machine Learning

This set of flashcards covers key concepts in machine learning, its processes, data types, and evaluation methods.

What is machine learning?

Machine learning is a sub-discipline of artificial intelligence that allows computers to learn from experience and improve performance on tasks without being explicitly programmed.

What are the three features of a successful machine learning application?

1. The class of tasks, 2. The measure of performance to be improved, 3. The source of experience.

What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train models, while unsupervised learning uses data without labels.

What are categorical and numerical data types?

Categorical data refers to non-numerical information grouped into categories, while numerical data is represented in number form and allows arithmetic operations.

What are examples of categorical data?

Nominal data (like names) and ordinal data (like education levels) are types of categorical data.

What are the steps involved in building a machine learning model?

1. Data collection and classification, 2. Data preprocessing, 3. Choosing a model, 4. Training, 5. Evaluation, 6. Prediction.

What is the purpose of model evaluation?

To assess the performance of the machine learning model on new data and compare the predicted results to actual values.

What is a training set in machine learning?

The training set is the portion of the data used to train the machine learning model, while the testing set is used to test the model's performance.