Introduction Some labeled training examples 4 USENET groups
Introduction
Some labeled training examples
4 USENET groups comp rec comp, sci talk Bag-of-words bit vector
Three types of iris flowers setosa versicolor virginica
red: setosa green: versicolor blue: virginica
Features permuted
Face detection
Regression
Unsupervised learning
Principal components dimensionality reduction 2 D linear subspace embedded in 3 D 2 D representation of the data
25 individual faces
Eigenfaces
Missing data (a) A noisy image with an occluder. (b) An estimate of the underlying pixel intensities, based on a pairwise Markov random field model.
Voronoi Tessellation Euclidean distance Manhattan distance
3 -NN
10 -nearest neighbors: red class red: class 1
10 -nearest neighbors: blue class blue: class 2
Maximum a posteriori of class labels blue: class 2
Polynomial Regression degree 14 degree 20
Sigmoid or logistic function sigm(−∞) = 0 sigm(0) = 0. 5 sigm(∞) = 1.
Sigmoid or logistic function sigm(−∞) = 0 sigm(0) = 0. 5 sigm(∞) = 1.
Logistic regression accept? • Solid black dots are SAT scores. • The open red circles are the predicted probabilities of acceptance. • The green crosses denote two students with the same SAT score of 525 • logistic regression is a form of classification, not regression! SAT scores
KNN K=1 K=5
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