Artificial Intelligence — definition?
Simulation of human intelligence by machines.
AI history — starting point?
1956 Dartmouth Conference, McCarthy coined the term.
Narrow AI — role?
Performs specific tasks within limited domains.
General AI — capability?
Hypothetical AI with human-like cognitive abilities.
Machine Learning — definition?
Systems improving from data without explicit programming.
Difference: AI vs ML?
AI is broader; ML is a subset focused on learning from data.
Supervised Learning — data type?
Labeled data with input-output pairs.
Unsupervised Learning — goal?
Find patterns or structure in unlabeled data.
Deep Learning — key feature?
Uses multi-layer neural networks for complex pattern recognition.
Neural Network — element?
Interconnected neurons organized in layers.
Model evaluation — metric?
Accuracy measures correct predictions over total.
Overfitting — meaning?
Model learns noise, performs poorly on new data.
AI applications — example?
Healthcare, autonomous vehicles, NLP, finance.
Clustering — technique?
Groups data based on similarity without labels.
Activation function — purpose?
Introduces non-linearity into neural networks.
Model testing — purpose?
Assess performance on unseen data to ensure generalization.
Testez vos connaissances avec un QCM de 8 questions sur Fundamentals of Artificial Intelligence and Machine Learning.
1. What is Artificial Intelligence (AI) primarily considered to be?
2. Who is credited with defining Machine Learning as systems that improve from data without being explicitly programmed, in 1959?
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