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Machine Learning

Algorithms that improve at a task by learning patterns from data instead of being explicitly programmed.

Machine Learning is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart TB
  D[(Data)] --> P{Learning paradigm}
  P --> S[Supervised]
  P --> U[Unsupervised]
  P --> SS[Self-supervised]
  P --> R[Reinforcement]
  S --> M[Train model]
  U --> M
  SS --> M
  R --> M
  M --> E[Evaluate] --> DEP[[Deploy]]

Key topics

  • Supervised learning

    Learn a mapping from inputs to labelled outputs — classification and regression.

  • Unsupervised learning

    Find structure in unlabelled data — clustering, dimensionality reduction, density estimation.

  • Self-supervised learning

    Create supervision from the data itself (e.g. predict the next token); the engine behind modern foundation models.

  • Reinforcement learning

    Learn by trial and error from rewards; covered in depth in its own area.

  • Core algorithms

    Linear/logistic regression, decision trees, SVMs, k-NN, naive Bayes, and ensembles like random forests and gradient boosting.

  • Feature engineering

    Turning raw data into informative inputs — scaling, encoding, selection, and extraction.

  • Training & evaluation

    Loss functions, gradient descent, regularization, cross-validation, and the bias–variance / over- vs under-fitting trade-off.

Foundations of AI · Deep Learning · Data & MLOps

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