This document summarizes key concepts from the CS 221 lecture on machine learning. It discusses supervised learning techniques like Naive Bayes classification, linear regression, perceptrons, and SVMs. It also covers unsupervised learning through k-nearest neighbors and discusses challenges like overfitting, generalization, and the curse of dimensionality.

1. CS 221: Artificial Intelligence Lecture 5: Machine Learning Peter Norvig and Sebastian Thrun

5. Machine Learning Lingo What? Parameters Structure Hidden concepts What from? Supervised Unsupervised Reinforcement Self-supervised What for? Prediction Diagnosis Compression Discovery How? Passive Active Online Offline Output? Classification Regression Clustering Details?? Generative Discriminative Smoothing

6. Supervised Machine Learning Given a training set: ( x 1 , y 1 ), ( x 2 , y 2 ), ( x 3 , y 3 ), … ( x n , y n ) Where each y i was generated by an unknown y = f ( x ), Discover a function h that approximates the true function f.

13. Spam Example P(spam | words) = e -76 / (e -76 + e -80.5 ) = 98.9% Word P(w|spam) P(w|ham) Tot Spam Tot Ham (prior) 0.33333 0.66666 -1.1 -0.4 Gary 0.00002 0.00021 -11.8 -8.9 would 0.00069 0.00084 -19.1 -16.0 you 0.00881 0.00304 -23.8 -21.8 like 0.00086 0.00083 -30.9 -28.9 to 0.01517 0.01339 -35.1 -33.2 lose 0.00008 0.00002 -44.5 -44.0 weight 0.00016 0.00002 -53.3 -55.0 while 0.00027 0.00027 -61.5 -63.2 you 0.00881 0.00304 -66.2 -69.0 sleep 0.00006 0.00001 -76.0 -80.5

26. Learning Model Parameters 1 0.1 2 0.1 3 0.1 4 0.1 5 0.1 6 0.1 7 0.1 8 0.1 9 0.1 0 0.1 1 0.01 2 0.05 3 0.05 4 0.30 5 0.80 6 0.90 7 0.05 8 0.60 9 0.50 0 0.80 1 0.05 2 0.01 3 0.90 4 0.80 5 0.90 6 0.90 7 0.25 8 0.85 9 0.60 0 0.80

27. Problem: Overfitting 2 wins!!

32. Regression: Minimizing Loss

33. Regression: Minimizing Loss Choose weights to minimize sum of squared errors

34. Regression: Minimizing Loss

35. Regression: Minimizing Loss y = w 1 x + w 0 Linear algebra gives an exact solution to the minimization problem

36. Linear Algebra Solution

37. Don ’t Always Trust Linear Models

38. Regression by Gradient Descent w = any point loop until convergence do: for each w i in w do: w i = w i – α ∂ Loss ( w ) ∂ w i

43. Linear Separator

44. Perceptron

46. What Linear Separator to Pick?

47. What Linear Separator to Pick? Maximizes the “margin” Support Vector Machines

52. Earthquakes and Explosions Using logistic regression (similar to linear regression) to do linear classification

53. K =1 Nearest Neighbors Using nearest neighbors to do classification

54. K =5 Nearest Neighbors Even with no parameters, you still have hyperparameters!

55. Curse of Dimensionality Average neighborhood size for 10-nearest neighbors, n dimensions, 1M uniform points

56. Curse of Dimensionality Proportion of points that are within the outer shell, 1% of thickness of the hypercube

58. Machine Learning Lingo What? Parameters Structure Hidden concepts What from? Supervised Unsupervised Reinforcement Self-supervised What for? Prediction Diagnosis Compression Discovery How? Passive Active Online Offline Output? Classification Regression Clustering Details?? Generative Discriminative Smoothing