Intro to AI 2 final terms (Lecture Machine Learning)

Overfitting

When a model performs well on training data but poorly on unseen data.

Underfitting

When a model performs poorly on both training and test data due to being too simple.

Generalization

The ability of a model to perform well on new, unseen data.

Regularization

A method to reduce overfitting by penalizing model complexity.

Bias

Error due to incorrect assumptions; high bias can cause underfitting.

Variance

Sensitivity to training data noise; high variance can cause overfitting.

Bias-Variance Tradeoff

Balancing simplicity and flexibility to optimize generalization.

Entropy

A measure of uncertainty or disorder in a dataset.

Information Gain

Reduction in entropy after a dataset is split on an attribute.

Decision Tree

A model that uses a tree-like graph to make decisions based on features.

Training Data

Data used to train a machine learning model.

Test Data

Data used to evaluate a trained model’s performance.

Validation Data

Data used to tune hyperparameters during training.

Supervised Learning

A type of machine learning where a model is trained on labeled data, meaning the input data is paired with the correct output.

Unsupervised Learning

A type of machine learning where a model is trained on data without labeled outputs, allowing it to discover patterns and relationships in the input data.

Semi-Supervised Learning

A type of machine learning that combines both labeled and unlabeled data for training, typically using a small amount of labeled data and a larger amount of unlabeled data to improve model performance.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties for the actions it takes.