Tuesday, January 19: Course outline. Probabilistic formulations of prediction problems: 01-notes.pdf: Thursday, January 21: Plug-in estimators. Empirical risk ... The Perceptron Algorithm 18 input: training data D output: weights w initialize w 0 while not converged aafor (x,y) 2 D aaaaif y(w · x) 0 aaaaaaw w + yx •the simplest machine learning algorithm •keep cycling through the training data •update w if there is a mistake on example (x, y) •until all examples are classiﬁed correctly x w w0 When m = 1, the algorithm is exactly the perceptron predictor W[n,1,h+1] holds n weights vectors When n = 1, the algorithm is path-based neural predictor W[1,m,h+1] holds m weights vectors Can be pipelined to reduce latency The design space in between contains more accurate predictors If n is small, predictor can still be pipelined to reduce ... What is instance based learning and K-Nearest Neighbour algorithm? 14 mins What is the standard distance function (euclidean distance) and the 3 issues related to it?