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Decision making in episodic environments

Decision making in episodic environments. We have just looked at decision making in sequential environments Now let’s consider the “easier” problem of episodic environments The agent gets a series of unrelated problem instances and has to make some decision or inference about each of them

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Decision making in episodic environments

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  1. Decision making in episodic environments • We have just looked at decision making in sequential environments • Now let’s consider the “easier” problem of episodic environments • The agent gets a series of unrelated problem instances and has to make some decision or inference about each of them • This is what most of “machine learning” is about

  2. Example: Image classification input desired output apple pear tomato cow dog horse

  3. Example: Spam Filter

  4. Example: Seismic data Earthquakes Surface wave magnitude Nuclear explosions Body wave magnitude

  5. The basic classification framework y = f(x) • Learning: given a training set of labeled examples{(x1,y1), …, (xN,yN)}, estimate the parameters of the prediction function f • Inference: apply f to a never before seen test examplex and output the predicted value y = f(x) output classification function input

  6. Example: Training and testing • Key challenge: generalization to unseen examples Training set (labels known) Test set (labels unknown)

  7. Naïve Bayes classifier A single dimension or attribute of x

  8. Decision tree classifier Example problem: decide whether to wait for a table at a restaurant, based on the following attributes: • Alternate: is there an alternative restaurant nearby? • Bar: is there a comfortable bar area to wait in? • Fri/Sat:is today Friday or Saturday? • Hungry: are we hungry? • Patrons: number of people in the restaurant (None, Some, Full) • Price: price range ($, $$, $$$) • Raining: is it raining outside? • Reservation: have we made a reservation? • Type: kind of restaurant (French, Italian, Thai, Burger) • WaitEstimate: estimated waiting time (0-10, 10-30, 30-60, >60)

  9. Decision tree classifier

  10. Decision tree classifier

  11. Nearest neighbor classifier f(x) = label of the training example nearest to x • All we need is a distance function for our inputs • No training required! Training examples from class 2 Test example Training examples from class 1

  12. Linear classifier • Find a linear function to separate the classes f(x) = sgn(w1x1 + w2x2 + … + wDxD) = sgn(w  x)

  13. Perceptron Input Weights x1 w1 x2 w2 Output:sgn(wx+ b) x3 w3 . . . wD xD

  14. Linear separability

  15. Multi-Layer Neural Network • Can learn nonlinear functions • Training: find network weights to minimize the error between true and estimated labels of training examples: • Minimization can be done by gradient descent provided f is differentiable • This training method is called back-propagation

  16. Differentiable perceptron Input Weights x1 w1 x2 w2 Output:(wx+ b) x3 w3 . . . Sigmoid function: wd xd

  17. Review: Types of classifiers • Naïve Bayes • Decision tree • Nearest neighbor • Linear classifier • Nonlinear classifier

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