Machine learning
Machine learning (ML) is a branch of computer science in which systems learn from data to improve their performance on a task, without being explicitly programmed with rules. Rather than following hand-written instructions, a machine learning model is trained on examples and learns to generalise from them.
ML encompasses a wide range of techniques and problem types:
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Supervised learning — The model is trained on labelled examples (input/output pairs) to learn a mapping from inputs to outputs. Used for classification (eg. spam detection, image recognition) and regression (eg. predicting house prices).
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Unsupervised learning — The model finds structure in unlabelled data. Used for clustering, dimensionality reduction, and anomaly detection.
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Reinforcement learning — An agent learns by interacting with an environment and receiving rewards or penalties. Used in robotics, game-playing, and fine-tuning language models.
Generative AI is a subfield of machine learning focused on models that produce new content — text, images, audio, and video — rather than classifying or predicting from existing data. Large language models (LLMs), such as GPT and Claude, are the most prominent example. They are neural networks trained using ML techniques on vast text datasets to generate human-like language. Modern LLMs are built on the transformer architecture.
References
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Designing Machine Learning Systems, Chip Huyen – Designing scalable, maintainable ML pipelines.
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AI Engineering, Chip Huyen – Building end-to-end AI products.
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Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig – The canonical AI theory text.
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Deep Learning, Ian Goodfellow, Yoshua Bengio & Aaron Courville – Mathematical foundations of neural networks.
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Deep Learning: Foundations and Concepts, Christopher Bishop & Hugh Bishop – A probability-grounded modern treatment (2024).
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Understanding Deep Learning, Simon Prince – Math, intuition, and Python notebooks.
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Speech and Language Processing, Dan Jurafsky & James H. Martin – The NLP reference, kept current through the deep-learning era.
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Reinforcement Learning: An Introduction, Richard Sutton & Andrew Barto – Foundations of reinforcement learning.