overview - Learning from data Overview I Machine learning...

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Overview 1 / 21 Learning from data Machine learning : study of computational mechanisms that “learn” from data in order to make predictions and decisions. 2 / 21 Example 1: image classification Birdwatcher takes pictures of birds, organizes photos by species. Goal : automatically recognize bird species in new photos. Indigo bunting 3 / 21 Example 2: recommender system Netflix users watch movies and provide ratings. Goal : predict the rating a user will provide on a movie not yet watched. ( Real goal : keep users paying customers.) Geared toward males Serious Escapist The Princess Diaries Braveheart Lethal Weapon Independence Day Ocean’s 11 Sense and Sensibility Gus Dave Geared toward females Amadeus The Lion King Dumb and Dumber The Color Purple (Graphic is from Koren, Bell, and Volinsky.) 4 / 21
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Example 3: machine translation Linguists provide translations of all English language books into French, sentence-by-sentence. Goal : automatically translate any English sentence into French. 5 / 21 Example 4: personalized medicine Physician attends to patients, prescribes treatments, and observes health outcomes (e.g., recovery, death). Goal : prescribe personalized treatment for patient that delivers best possible health outcomes. 6 / 21 Basic setting Data : labeled examples ( x 1 , y 1 ) , ( x 2 , y 2 ) , . . . , ( x n , y n ) Inputs × Labels X × Y where each input x i is a description of an instance (e.g., image, (user,movie), sentence, patient), and each corresponding label y i is an annotation relevant to the task (typically not easy to automatically obtain). Goal : “learn” a function ˆ f : Inputs Actions ˆ f : X → A from the data, such that for a new input x (usually without seeing its corresponding label y ), the action ˆ f ( x ) is a “good” action. Typically, for a prediction problem , we have Actions = Labels A = Y (i.e., we want the function to predict the labels of new inputs).
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