Fundamentals of Artificial Intelligence

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Artificial Intelligence (AI)

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The simulation of human intelligence processes by machines, particularly computer systems.

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Machine Learning (ML)

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A subset of AI focused on developing algorithms that enable computers to learn from and make predictions based on data.

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Supervised Learning

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A learning approach where algorithms are trained on labelled datasets to classify or predict outcomes.

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Unsupervised Learning

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A method used to identify hidden structures in unlabelled data, often employing techniques like clustering.

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Semi-Supervised Learning

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A training approach combining limited labelled data with extensive unlabelled datasets to improve its performance on specific tasks.

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Reinforcement Learning

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A type of learning where an agent makes decisions based on feedback from interactions with an environment, aiming to maximize rewards.

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Deep Learning (DL)

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An advanced subset of ML using artificial neural networks to analyse complex patterns in large datasets.

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Discriminative Models

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Models that are trained to classify or label existing data rather than generate new data.

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Prompting

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A technique used in generative AI where users provide natural language instructions to guide the output generation.

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Natural Language

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The language that humans use for everyday communication, as opposed to formal languages used in programming.