AI GlossaryMachine LearningENTRY — AI Fundamentals
[AI Fundamentals]
Machine Learning.
A subset of AI where systems automatically learn and improve from experience without being explicitly programmed.
In-depth explanation
01Machine learning algorithms build mathematical models based on training data to make predictions or decisions. The three main types are supervised learning (learning from labeled examples), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error). ML powers many modern applications from email filters to medical diagnosis.
Examples
02EX. 01
Email spam filters
EX. 02
Product recommendations
EX. 03
Fraud detection
More in AI Fundamentals
0301AccuracyAccuracy is a metric used in machine learning to measure the percentage of correctly predicted instances in relation to the total number of instances evaluated. It is widely used to assess the performance of classification models.02Active LearningActive learning is a machine learning approach where the algorithm selectively queries a human expert to label new data points with the goal of improving the model's performance with minimal labeled data.03Adam OptimizerAdam (Adaptive Moment Estimation) is an optimization algorithm used in training machine learning models, particularly neural networks. It combines the advantages of two other extensions of stochastic gradient descent, specifically AdaGrad and RMSProp, to adaptively adjust the learning rate of each parameter.04Adversarial AttackAn adversarial attack is a deliberate attempt to manipulate the inputs to an AI model in order to cause it to make errors or incorrect predictions, often by introducing subtle perturbations that are imperceptible to humans.05Adversarial ExampleAn adversarial example is a specially crafted input designed to deceive a machine learning model, causing it to make an incorrect prediction or classification.06Agentic AIAgentic AI refers to artificial intelligence systems designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals.
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