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: INTRODUCTION TO Machine Learning Parametric Methods

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Parametric Estimation X = { x t } t where x t ~ p (x) Parametric estimation: Assume a form for p (x |q ) and estimate q, its sufficient statistics, using X N ( μ, σ 2 ) where q = { μ, σ 2 }

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Maximum Likelihood Estimation Likelihood of q given the sample X l ( θ |X) = p (X | θ ) = t p (x t | θ ) Log likelihood L( θ |X) = log l ( θ |X) = t log p (x t | θ ) Maximum likelihood estimator θ * = argmax θ L( θ |X)

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Examples: Bernoulli/Multinomial Bernoulli: Two states, failure/success, x in {0,1} P (x) = p o x (1 – p o ) (1 – x) L (p o |X) = log t p o x t (1 – p o ) (1 – x t ) MLE: p o = t x t / N Multinomial: K>2 states, x i in {0,1} P (x 1,x 2,...,x K ) = i p i x i L(p 1,p 2,...,p K |X) = log t i p i x i t MLE: p i = t x i t / N

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Gaussian (Normal) Distribution p(x) = N ( μ, σ 2 ) MLE for μ and σ 2 :

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Bias and Variance Unknown parameter q Estimator d i = d (X i ) on sample X i Bias: b q (d) = E [d] – q Variance: E [(d–E [d]) 2 ] Mean square error: r (d,q) = E [(d–q) 2 ] = (E [d] – q) 2 + E [(d–E [d]) 2 ] = Bias 2 + Variance

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Bayes Estimator Treat θ as a random var with prior p ( θ ) Bayes rule: p ( θ |X) = p(X| θ ) p( θ ) / p(X) Full: p(x|X) = p(x| θ ) p( θ |X) d θ Maximum a Posteriori (MAP): θ MAP = argmax θ p( θ |X) Maximum Likelihood (ML): θ ML = argmax θ p(X| θ ) Bayes: θ Bayes = E[ θ |X] = θ p( θ |X) d θ

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Parametric Classification

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Given the sample ML estimates are Discriminant becomes Parametric Classification

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(a)and(b) for two classes when the input is one-dimensional. Variances are equal and the posteriors intersect at one point, which is the threshold if decision.

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Parametric Classification (a)and(b) for two classes when the input is one-dimensional. Variances are unequal and the posteriors intersect at two points. In (c), the expected risks are shown for the two classes and for reject with

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Regression

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Regression: From LogL to Error

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Linear Regression

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Polynomial Regression

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Square Error: Relative Square Error: Absolute Error: E ( θ |X) = t |r t – g(x t | θ )| ε -sensitive Error: E ( θ |X) = t 1(|r t – g(x t | θ )|>ε) (|r t – g(x t |θ)| – ε) Other Error Measures

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Bias and Variance biasvariance noisesquared error

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Estimating Bias and Variance M samples X i ={x t i, r t i }, i=1,...,M are used to fit g i (x), i =1,...,M

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Bias/Variance Dilemma Example: g i (x)=2 has no variance and high bias g i (x)= t r t i /N has lower bias with variance As we increase complexity, bias decreases (a better fit to data) and variance increases (fit varies more with data)

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Bias/Variance Dilemma (a) Function, f(x) = 2sin(1.5x), and one noisy (N(0,1)) dataset sampled from the function. Five samples are taken, each containing twenty in-stances. (b), (c), (d) are five polynomial fits, namely, gi(.), of order 1, 3 and 5. for each case, dotted line is the average of the five fits namely,.

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Polynomial Regression Best fit min error In the same setting as that of previous, using one hundred models instead of five, bias, variance, and error for polynomials of order 1 to 5.

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Model Selection Cross-validation: Measure generalization accuracy by testing on data unused during training Regularization: Penalize complex models E=error on data + λ model complexity Akaikes information criterion (AIC), Bayesian information criterion (BIC) Minimum description length (MDL): Kolmogorov complexity, shortest description of data Structural risk minimization (SRM)

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Best fit, elbow Model Selection

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Bayesian Model Selection Prior on models, p(model) Regularization, when prior favors simpler models Bayes, MAP of the posterior, p(model|data) Average over a number of models with high posterior

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Regression example Coefficients increase in magnitude as order increases: 1: [-0.0769, 0.0016] 2: [0.1682, -0.6657, 0.0080] 3: [0.4238, -2.5778, 3.4675, -0.0002 4: [-0.1093, 1.4356, -5.5007, 6.0454, -0.0019]

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