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the feasibility of machine learning

the feasibility of machine learning . Component of learning. Formalization Input （输入） :X (customer application) think of it as deed dimension vector Output （输出） :Y(+1,-1) good/bad customer Target Function( 目标函数 ) : f :x → y ideal credit approval formula. Component of learning.

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the feasibility of machine learning

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  1. the feasibility of machine learning

  2. Component of learning • Formalization • Input（输入）:X (customer application) think of it as deed dimension vector • Output（输出）:Y(+1,-1) good/bad customer • Target Function(目标函数) : f :x→y ideal credit approval formula Company Logo

  3. Component of learning • Formalization • Data（数据）: (), (),…, () historical records ↓↓↓ • Hypothesis(假设) ：g :x→y 为了得到目标函数的公式 F is unknown G is very much known actually we created it Company Logo

  4. Component of learning UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (), (),…, () FINAL HYPOTHESIS g (G hopefully approximates F) Company Logo

  5. Component of learning UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (), (),…, () FINAL HYPOTHESIS g (G hopefully approximates F) Company Logo

  6. Component of learning TRAINING EXAMPLES (), (),…, () LEARNING ALGORITHM →HYPOTHESIS SET (从现实模型公式中创造公式） （将它们成为假设集） FINAL HYPOTHESIS Company Logo

  7. Component of learning HYPOTHESIS SET H 从假设集选出一个假设 H 衍生出一堆H’s(待定函数) Company Logo

  8. Component of learning UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (), (),…, () FINAL HYPOTHESIS g (G hopefully approximates F) Company Logo

  9. HYPOTHESIS SET • Hypothesis Set • H = {h} g∈ H • Learning Algorithm • Together. they are referred to as the learning model. a hypothesis set and a learning algorithm Company Logo

  10. A simplehypothesis set—’perceptron’ • For input X= attributes of a customer • Approve credit if > threshold • Deny credit if < threshold This linear formula h∈ H can be written as h(x) = sign( ()– threshold ) Company Logo

  11. Learning Feasible Company Logo

  12. Learning Feasible • A related experiment P(picking red)=μ P(picking green)=1-μ μ=probability of red marbles Company Logo

  13. Learning Feasible Pick N marbles independently The fraction of red marbles in sample =v Company Logo

  14. Does v Say anything about μ? • NO! Sample can be mostly green while bin is mostly red Company Logo

  15. Does v Say anything about μ? • Yes Sample frequency v is close to bin frequency μ This is called Hoeffding Inequality Company Logo

  16. Learning Feasible • Bin • Unknown is a number μ • Learning • Unknown is a function f:x→y each marble is a point x ∈ X Company Logo

  17. Learning Feasible • Bin • Unknown is a number μ • Learning • Unknown is a function f:x→y each marble is a point x ∈ X Hypothesis got it right h(x)=f(x) Company Logo

  18. Learning Feasible • Bin • Unknown is a number μ • Learning • Unknown is a function f:x→y each marble is a point x ∈ X Hypothesis got it wrong h(x)≠f(x) Company Logo

  19. Learning Feasible UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (), (),…, () FINAL HYPOTHESIS g (G hopefully approximates F) Company Logo

  20. Learning Feasible Probability distribution P on X Company Logo

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