A foundations hub for the core machine-learning concepts that legal, compliance, and product teams need before reviewing AI systems.
30
Terms
10
Comparisons
8
Questions
Machine learning foundations explain how models learn from data, how datasets are split, and why model behavior depends on training choices. This topic helps non-specialists understand enough technical context to ask better compliance questions.
Many legal and governance disputes come from basic misunderstandings about training data, model behavior, validation, bias, and generalization.
Useful for AI literacy, procurement review, model documentation, bias analysis, audit preparation, and cross-functional training.
Core approaches that describe how systems learn from labels, data, feedback, or interaction.
Common model families and algorithmic building blocks used across AI systems.
Dataset and training concepts that shape model quality and governance risk.
A side-by-side comparison of Supervised Learning and Unsupervised Learning. Understand how learning from labeled examples differs from finding patterns in data without pre-existing labels.
A side-by-side comparison of Reinforcement Learning and Supervised Learning. It explains how learning from rewards in an environment differs from learning mappings from labeled examples.
A side-by-side comparison of Supervised Learning and Semi-Supervised Learning. Understand how learning from labeled examples differs from combining labeled and unlabeled data.
A side-by-side comparison of Self-supervised Learning and Semi-Supervised Learning. Understand how each uses unlabeled data and why the role of labels differs.
A side-by-side comparison of Machine Learning and Deep Learning. Understand how the broader field of learning from data differs from neural-network methods with multiple layers.
A side-by-side comparison of Training Data and Test Set. Understand why the data used to fit a model must be separated from the reserved data used to evaluate it.
A side-by-side comparison of Training Data and Validation Data. Understand how data used to fit the model differs from data used during development to tune choices and detect generalization problems.
A side-by-side comparison of Overfitting and Underfitting. It explains how learning training data too closely differs from failing to learn meaningful patterns in the data.
A side-by-side comparison of Classification and Regression. Understand how predicting discrete categories differs from predicting continuous or numerical values.
A side-by-side comparison of Decision Tree and Random Forest. Understand how the terms differ, when each applies, and what the distinction means for AI governance, system design, or assurance evidence.
Self-supervised learning is a machine learning approach that creates training signals from unlabeled data itself. It converts aspects of an unsupervised problem into supervised learning by generating surrogate labels, making it useful when labeled data is scarce but unlabeled data is abundant.
Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data during training. It is useful when labeled examples are costly or limited but larger volumes of unlabeled data can help improve representation, classification, or prediction.
Training data is the dataset used to teach or fit a machine learning model by adjusting its learnable parameters. Its quality, quantity, diversity, labeling, and provenance strongly influence the performance, reliability, and compliance posture of the resulting model.
Overfitting occurs when a model learns the training data too closely, including noise or idiosyncratic details, and performs poorly on new data. It is commonly indicated by high training performance but weaker validation or test performance.
Underfitting occurs when a model is too simple or insufficiently trained to capture meaningful patterns in the data. An underfit model performs poorly on both training data and new data because it has not learned the underlying relationships needed for the task.
A decision tree is an interpretable supervised learning model that represents decision rules in a tree-like structure. Inference follows a path from a root node through feature-based conditions to a leaf node that provides the predicted class or value.
Deep learning is a subfield of machine learning that uses neural networks with multiple layers to learn representations and patterns from data.
Use Classification when the target is a category; use Regression when the target is a continuous or numerical value.