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Machine Learning Motivation for machine learning How to set up a problem How to design a learner Introduce one class of learners (ANN) –Perceptrons –Feed-forward network Back-prop –Other types of networks

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Components of a “Well-Posed” Learning Problem Task: the domain of the problem Experience: information about the domain Performance measure: a metric to judge how well the trained system can solve the problem Learner: a computer program whose performance on the task improves (according to the metric) with more experience

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Example: Classification Task: Predict whether the user might like a movie or not Experience: database of movies the user has seen and the user’s ratings for them Performance Measure: percent of times the system correctly predicts the user’s preference

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Example: Speech Recognition Task: take dictations from the user Experience: a collection of recordings of acoustic utterances with their transcriptions Performance Measure: percent of words correctly identified

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Example: Function Modeling Task: approximate an unknown function f(x) Experience: a set of data points: {x i, f(x i )} Performance Measure: average error rate between f(x), the target function, and h(x), the function the system learned, over m test points e.g.

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Designing a Learner Training experience –Kind of feedback? –A representative sample? –Learner has control? Target function –Specify expected behavior Function representation –Specify form and parameters Learning algorithm

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Artificial Neural Networks Inspired by neurobiology A network is made up of massively interconnect “neurons” Good for some learning problems –Noisy training examples (contain errors) –Target function input can be best described by a vector (e.g., robot sensor data) –Target function is continuous (differentiable)

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Perceptron w0w0 w1w1 wnwn 1 x1x1 xnxn O={-1,+1} O = g(In) = g(xw) = +1 : In > -1: otherwise n weighted inputs: In = w 0 +x 1 w 1 + x 2 w 2 + … + x n w n = x w An activation function, g(In) …

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Training a Perceptron Quantify error –compare output with correct answer Update weights to minimize error is a constant, the learning rate

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How Powerful Are Perceptrons? A perceptron can represent simple Boolean functions –AND, OR, NOT A network of perceptron can represent any Boolean function A perceptron cannot represent XOR –Why?

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Linearly Separable Refer to pictures from R&N Fig. 19.9

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Gradient Descent Guarantees convergence Approximates non-linearly separable functions Search through the weight space Define error as a continuous function of the weights

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Multilayer Network x1x1 x2x2 x n … Input units Hidden units Output units uiui … ujuj w ij OjOj … w ni

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Training a Multilayer Network Need to update weights to minimize error, but… –How to assign portions of “blame” to each weights fairly? –In a multilayer network, a weight may (eventually) contribute to multiple outputs –Need to back-propagate the error

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Back-Propagation Between a hidden unit and an output unit: Between an input unit and a hidden unit:

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Artificial Neural Network Summary Expressiveness: Can approximate any function of a set of attributes Computational efficiency: May take a long time to train to convergence Generalization: generalizes well Sensitivity to noise: very tolerant Transparency: can be used like a black box Prior knowledge: difficult to incorporate

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