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Probability is a crucial area of mathematics focusing on the likelihood of events, which ranges from 0 (impossible) to 1 (certain). The chapter outlines fundamental probability concepts, including theoretical and experimental probability, and explores their applications in real-life situations, particularly in artificial intelligence. Understanding probability is essential for making informed decisions and ethical considerations in AI systems.
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u3ch8.pdfClass Notes
Memorization
What we have learnt
- Probability measures the li...
- Theoretical Probability is ...
- Experimental Probability re...
Final Test
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What we have learnt
- Probability measures the likelihood of events, ranging from 0 (impossible) to 1 (certain).
- Theoretical Probability is calculated using the formula: P(E) = Number of favourable outcomes / Total outcomes.
- Experimental Probability relies on the results of trials.
- Probability has significant applications in AI, including prediction, diagnosis, and decision-making.
- Understanding probability is key to creating intelligent and fair AI systems.
Key Concepts
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Term: Probability
Definition: A measure of how likely an event is to occur, ranging from 0 to 1.
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Term: Sample Space (S)
Definition: The set of all possible outcomes of an experiment.
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Term: Favourable Outcome
Definition: An outcome that contributes to the success of an event.
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Term: Empirical Probability
Definition: Probability based on actual experiments or observations.
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Term: Complementary Events
Definition: If A is an event, then its complement (A') is the event that A does not occur.
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Term: Applications of Probability in AI
Definition: Probability is used in various AI applications such as machine learning, robotics, and natural language processing.