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A Search Tree Enumerates Possibili0es 6 0002 LECTURE 2 6 Take Don'tTake Left-first, depth-first enumera0on Val = 170 Cal = 766 Val = 120 Cal = 766

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Lawrence Solomon, Financial Post, April 15, 2013 6 0002 LECTURE 15 6 Moral Cherry picking image © source unknown All rights reserved This content is 

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that the past predicts the future Interested in extending to programs that can infer useful informaion from paterns in data 6 0002 LECTURE 11 Slide 6 

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Variability of subgroups less than of entire population ▫Requires care to do properly ▫Well stick to simple random samples 6 0002 LECTURE 8 6 

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TEXTBOOKS/TUTORIALS • Dive Into Python - another survey of Python syntax, datatypes, etc • Think Python by Allen Downey - a good general overview of the  

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the label of a new example ◦ Find the nearest example in the training data ◦ Predict the label associated with that example X 6 0002 LECTURE 13 6 

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one fewer cluster 3 Continue the process until all items are clustered into a single cluster of size N What does distance mean? 6 0002 LECTURE 12 6 

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in a molecule are related to one another ◦ Ancestral rela1onships 6 0002 LECTURE 3 6 For more information, see https://ocw mit edu/help/faq-fair-use

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Drunkard's Walk 6 0002 LECTURE 5 4 Page 5 One Possible First Step 6 0002 LECTURE 5 5 Page 6 Another Possible First Step 6 0002 LECTURE 5 6 

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def distFrom(self, other): xDist = self.x -other.getX() yDist = self.y -other.getY() return (xDist**2 + yDist**2)**0.5 def __str__(self): return '<' + str(self.x) + ', ' +str(self.y) + '>'

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import random class UsualDrunk (Drunk): def takeStep(self): stepChoices = [(0,1), (0,-1), (1, 0), (-1, 0)] return random.choice(ste pChoices) class MasochistDrunk(Drunk): def takeStep(self): stepChoices = [(0.0,1.1), (0.0,-0.9), (1.0, 0.0), ( -1.0, 0.0)] return random.choice(stepChoices)

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def walk(f, d, numSteps): """Assumes: f a Field, d a Drunk in f, and numSteps an int >= 0.

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def simWalks (numSteps, numTrials, dClass): """Assumes numSteps an int >= 0, numTrials an int > 0, dClass a subclass of Drunk

Simulates numTrials walks of numSteps steps

each. Returns a list of the final distances for each trial"""

Homer = dClass()

origin = Location(0, 0) distances = [] for t in range(numTrials): f = Field() f.addDrunk(Homer, origin) distances.append(round(walk(f, Homer, numTrials), 1)) return distances

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def drunkTest (walkLengths, numTrials, dClass): """Assumes walkLengths a sequence of ints >= 0 numTrials an int > 0, dClass a subclass of Drunk

For each number of steps in walkLengths

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