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Machine learning is a branch of artificial intelligence that focuses on creating systems that learn from data, with key methods including supervised and unsupervised learning. Important concepts include overfitting, cross-validation, and feature engineering, along with common tools like scikit-learn, TensorFlow, and PyTorch. Ethical considerations in machine learning emphasize fairness, accountability, and transparency.

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Introduction to Machine Learning - Class Notes

Machine learning is a subfield of artificial intelligence focused on building systems that learn from

data.

Supervised learning involves training a model on labeled data, while unsupervised learning uses

unlabeled data.

Key algorithms include linear regression, decision trees, support vector machines, and neural

networks.

Overfitting occurs when a model learns the training data too well and fails to generalize.

Cross-validation is a method used to evaluate the performance of a machine learning model.

Feature engineering is the process of selecting and transforming variables to improve model

performance.

Common tools include scikit-learn, TensorFlow, and PyTorch.

Ethical considerations include fairness, accountability, and transparency in AI systems.

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Introduction to Machine Learning - Class Notes

Cross-validation is a method used to evaluate the performance of a machine learning model.

Common tools include scikit-learn, TensorFlow, and PyTorch.

Ethical considerations include fairness, accountability, and transparency in AI systems.

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