ML Fundamentals
Bayesian Networks
A diagram that maps out cause-and-effect relationships and probabilities (e.g., "If it rains, grass is 90% likely wet").
Definition
Probabilistic graphical models that represent conditional dependencies between random variables using directed acyclic graphs. They use Bayes' theorem to update probabilities as new evidence is available.
Why it matters
Powerful for reasoning under uncertainty, especially in medicine and diagnosis.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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