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proibability

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Machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data and make decisions without being explicitly programmed. It uses algorithms to identify patterns, recognize trends, and improve performance over time. Machine learning is widely used in various applications, including image recognition, natural language processing, recommendation systems, and predictive analytics. It can be categorized into supervised learning, where models learn from labeled data, unsupervised learning, where patterns are discovered in unlabeled data, and reinforcement learning, where agents learn through rewards and penalties. As machine learning continues to advance, it plays a crucial role in transforming industries and enhancing automation.

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Institution
GATE
Course
GATE

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Probability Theory for
Machine Learning
Chris Cremer
September 2015

,Outline
• Motivation
• Probability Definitions and Rules
• Probability Distributions
• MLE for Gaussian Parameter Estimation
• MLE and Least Squares

,Material
• Pattern Recognition and Machine Learning - Christopher M. Bishop
• All of Statistics – Larry Wasserman
• Wolfram MathWorld
• Wikipedia

, Motivation
• Uncertainty arises through:
• Noisy measurements
• Finite size of data sets
• Ambiguity: The word bank can mean (1) a financial institution, (2) the side of a ri
or (3) tilting an airplane. Which meaning was intended, based on the words that
appear nearby?
• Limited Model Complexity
• Probability theory provides a consistent framework for the quantificatio
and manipulation of uncertainty
• Allows us to make optimal predictions given all the information availabl
us, even though that information may be incomplete or ambiguous
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Uploaded on
March 16, 2025
Number of pages
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Written in
2024/2025
Type
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Subjects

  • probability
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