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# Probability And Stats Intro

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• 1. Crash course in probability theory and statistics – part 1 Machine Learning, Mon Apr 14, 2008
• 2. Motivation Problem: To avoid relying on “magic” we need mathematics. For machine learning we need to quantify: ●Uncertainty in data measures and conclusions ●“Goodness” of model (when confronted with data) ●Expected error and expected success rates ●...and many similar quantities...
• 3. Motivation Problem: To avoid relying on “magic” we need mathematics. For machine learning we need to quantify: ●Uncertainty in data measures and conclusions ●“Goodness” of model (when confronted with data) ●Expected error and expected success rates ●...and many similar quantities... Probability theory: Mathematical modeling when uncertainty or randomness is present. P  X = x i , Y = y j = pij