机器学习前导---IBM讲座笔记

本文探讨了机器学习的主要应用场景,如推荐系统、计算机视觉等,并深入解释了监督学习与非监督学习的区别及其典型任务,例如分类、回归、聚类等。此外,还介绍了支持向量机(SVM)的基本原理及其实现方法。

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  1. Machinelearning Application:

Recomendation

Compute Vision

Diriveless Car

Web Search

Speech Recagnition

Question Answering

Game Player

Smart Healthcare


Supervised learningand Unsupervised learning

eg:

classification,regression clustreing,association analysis


Machine learningtasks(problem types)

supervisedlearning=predict a target y from input x

(and seml-supervisedlearning)y represents a catrgory or “class”

=>classification


binary:y belongs to{-1,+1} or y belongs to (0,1)

multickass:y belongsto {1,m} or y belongs to {0,m-1}


y is a real-valuenumber

=>regression ybelongs to R or y belongs to R^m


Above is predictivemodels

Below isDesctrisptive models


Unsupervisedlearning :no explicit prediction target y

model the probablitydistribution of x

=>densityestimation


discover underlyingstructure in data

=>clustering

=>demensionalityreduction

=>(unsupervised )representation learning


trainging:we learn apredictive function f by optimizing it so that it predicts well onthe traing set


Use forprediction:we can then use in new inputs that were not part of thetraing set

关键词:generalize:泛化


the goal of learningis not to learn perfectly (memorizing) the traing set.What'simportant is the ability for the predictor to generide well on new


problem dimensions:

number of explits

input demensionditynumber if input features

characterizing eachexample

targetdimensionality ex:number of classes


under-fitting andoverfitting

关键词:fitting:拟合


performance ontraining set is not a good estimate of generalization


support vectormachine(SVM支持向量机)

LogisticRegression(逻辑斯蒂回归)


SVM

A powerful methodfor classification


Bettergeneralization

key ideas


--use kernelfunctionto transform low dimensional training sample to higherdimensions

--use quadraticprogramming (QP二次规划)to find the best classifier


support vectors arethose dataprints that the margin pushes up against


the soft margin SVMis equivalent to applying a hinge loss


empirical loss +regularization 损失函数与正则化

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