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Machine learning

Machine learning. Image source: https://www.coursera.org/course/ml. Machine learning. Definition Getting a computer to do well on a task without explicitly programming it Improving performance on a task based on experience. Learning for episodic tasks.

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Machine learning

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  1. Machine learning Image source: https://www.coursera.org/course/ml

  2. Machine learning • Definition • Getting a computer to do well on a task without explicitly programming it • Improving performance on a task based on experience

  3. Learning for episodic tasks • We have just looked at learning 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

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

  5. Learning for episodic tasks • We have just looked at learning 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 • In this case, “experience” comes in the form of training data

  6. Training data apple • Key challenge of learning: generalization to unseen examples pear tomato cow dog horse

  7. Example 2: Spam filter

  8. Example 3: Seismic data classification Earthquakes Surface wave magnitude Nuclear explosions Body wave magnitude

  9. The basic machine learning 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

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

  11. 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)

  12. Decision tree classifier

  13. Decision tree classifier

  14. 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

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

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

  17. Linear separability

  18. 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

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

  20. NY Times article YouTube video

  21. NY Times article

  22. Unsupervised Learning • Deep learning relies a lot on unsupervised learning • Idea: Given only unlabeled data as input, learn some sort of structure • The objective is often more vague or subjective than in supervised learning. This is more of an exploratory/descriptive data analysis

  23. Unsupervised Learning • Clustering • Discover groups of “similar” data points

  24. Unsupervised Learning • Quantization • Map a continuous input to a discrete (more compact) output 2 1 3

  25. Unsupervised Learning • Dimensionality reduction, manifold learning • Discover a lower-dimensional surface on which the data lives

  26. Unsupervised Learning • Density estimation • Find a function that approximates the probability density of the data (i.e., value of the function is high for “typical” points and low for “atypical” points) • Can be used for anomaly detection

  27. Other types of learning • Semi-supervised learning:lots of data is available, but only small portion is labeled (e.g. since labeling is expensive) • Why is learning from labeled and unlabeled data better than learning from labeled data alone? ?

  28. Other types of learning • Active learning: the learning algorithm can choose its own training examples, or ask a “teacher” for an answer on selected inputs S. Vijayanarasimhan and K. Grauman, “Cost-Sensitive Active Visual Category Learning,” 2009

  29. Structured Prediction Word Image Source: B. Taskar

  30. Structured Prediction Parse tree Sentence Source: B. Taskar

  31. Structured Prediction Word alignment Sentence in two languages Source: B. Taskar

  32. Structured Prediction Bond structure Amino-acid sequence Source: B. Taskar

  33. Structured Prediction • Many image-based inference tasks can loosely be thought of as “structured prediction” model Source: D. Ramanan

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