type
status
date
slug
summary
category
tags
icon
password
Here is the preface of the article: This note refers to Andrew Ng’s machine learning tutorial, so it is in English.
Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.
Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Machine Learning algorithms:
Supervised Learning(监督学习)
we give the algorithm a data set in which the “right answers” are given, and the task of the algorithm is to just produce more of these right answers.
To define with a bit more terminology,this is also called a regression problem(predict a continuous valued output).
In this example,We give it a data set of houses, and for each example in this data set, we tell it what the right price is or what is the actual price that the house sold for.the output is the price.
This is an example of classification problem.The term classification refers to the fact that here we’re trying to predict a discrete(离散) valued output: zero or one,malignant(恶性) or benign(良性).
And it turns out that in classification problems,sometimes you can have more than two possible values for the output. As a concrete example,maybe there are three types of breast cancers,and so you may try to predict the discrete value of zero,one,two,or three.
Unsupervised Learning
In Unsupervised Learning,we’re given data that looks different,data that looks like this,that doesn’t have any labels or that all has the same label or really no labels.
So we’re given the data set and we’re not told what to do with it,and we are not told what each data point is.Instead we’re just told,here is a data set,can you find some structure in the data?
Given this data set,an unsupervised learning algorithm might decide that the data lives in two different clusters(簇、集群),and the unsupervised learning algorithm may break these data into two separate clusters. So this is called a clustering algorithm.
Because we’re not giving the algorithm the right answer for the examples in my data set,this is unsupervised learning.
Another example is cocktail party problem,which is a classic problem in signal processing and source separation. It refers to the challenge of extracting individual sound sources from a mixture of multiple overlapping sound signals.
In Octave, you can implement it with a single line of code.
others: Reinforcement learning(强化学习),Recommender systems.
Written on the back: The cover picture is reproduced from the artist of pixiv Valya