02.pdf

N/A

Linear Classifiers and Perceptrons 7 2 Linear Classifiers and Perceptrons CLASSIFIERS You are given sample of n observations [aka examples], each with d features [aka predictors]. Some observations belong to class C; some do not. Example: Observations are ice cream lovers Features are height & age (d = 2) Some are in class “chocolate,” some prefer vanilla Goal: Predict preferred flavor based on their height & age. Represent each observation as a point in d-dimensional space, called a sample point /…

related reading

• perceptron learning algo, max margin classifierspeople.eecs.berkeley.edu
• 6.036_notes.pdfphillipi.github.io
• CS231n Deep Learning for Computer Visioncs231n.github.io
• soft margin svmspeople.eecs.berkeley.edu
• note 6: bayes decision rulepeople.eecs.berkeley.edu
• Classificationfairmlbook.org
• note 7: gaussian discriminant analysis, qda, ldapeople.eecs.berkeley.edu
• Neural networks and deep learningneuralnetworksanddeeplearning.com
• decision treespeople.eecs.berkeley.edu
• A Course in Machine Learningciml.info
• The Little Book of Deep Learningfleuret.org
• nn-notes.pdfboris-hanin.github.io