机器学习中的统计学方法. 从机器学习的核心视角来看,优化(optimization)和统计(statistics)是其最最重要的两项支撑技术.统计的方法可以用来机器学习,比如:聚类.贝叶斯等等,当然机器学习还有很多其他的方法,如神经网络(更小范围).SVM. 机器学习约等于统计+优化,它可以看作是一个方法,用来进行模式识别或数据挖掘.但对于统计和运筹学这俩门基础学科来说,又是应用(见下面四类问题),它大量地用到了统计的模型如马尔可夫随机场(Markov Random Field--MRF),最后…

昨天总结了深度学习的资料,今天把机器学习的资料也总结一下(友情提示:有些网站需要"科学上网"^_^) 推荐几本好书: 1.Pattern Recognition and Machine Learning (by Hastie, Tibshirani, and Friedman's ) 2.Elements of Statistical Learning(by Bishop's) 这两本是英文的,但是非常全,第一本需要有一定的数学基础,第可以先看第二本.如果看英文觉得吃力,推荐看一下下面…

In recent years, Kernel methods have received major attention, particularly due to the increased popularity of the Support Vector Machines. Kernel functions can be used in many applications as they provide a simple bridge from linearity to non-linear…

https://jmetzen.github.io/2015-01-29/ml_advice.html Advice for applying Machine Learning This post is based on a tutorial given in a machine learning course at University of Bremen. It summarizes some recommendations on how to get started with machin…

转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…

转载:http://dataunion.org/8463.html?utm_source=tuicool&utm_medium=referral <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智…

转载:http://www.jianshu.com/p/b73b6953e849 该资源的github地址:Qix <Statistical foundations of machine learning> 介绍:<机器学习的统计基础>在线版,该手册希望在理论与实践之间找到平衡点,各主要内容都伴有实际例子及数据,书中的例子程序都是用R语言编写的. <A Deep Learning Tutorial: From Perceptrons to Deep Networks>…

Machine learning Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions,…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…

Regularization method(正则化方法) Outline Overview of Regularization L0 regularization L1 regularization L2 regularization Elastic Net regularization L2,1 regularization Model example Reference Overview of Regularization Main goal: 1. Prevent over-fitting…

Machine Learning Methods: Decision trees and forests This post contains our crib notes on the basics of decision trees and forests. We first discuss the construction of individual trees, and then introduce random and boosted forests. We also discuss…

Ha, it's English time, let's spend a few minutes to learn a simple machine learning example in a simple passage. Introduction What is machine learning? you design methods for machine to learn itself and improve itself. By leading into the machine lea…

https://www.analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling/ http://normaldeviate.wordpress.com/2012/06/12/statistics-versus-machine-learning-5-2/ https://www.quora.com/What-is-the-difference-between-statistics-and-m…

机器学习中遗忘的数学知识 最大似然估计( Maximum likelihood ) 最大似然估计,也称为最大概似估计,是一种统计方法,它用来求一个样本集的相关概率密度函数的参数.这个方法最早是遗传学家以及统计学家罗纳德·费雪爵士在1912年至1922年间开始使用的. 最大似然估计的原理 给定一个概率分布,假定其概率密度函数(连续分布)或概率质量函数(离散分布)为,以及一个分布参数,我们可以从这个分布中抽出一个具有个值的采样,通过利用,我们就能计算出其概率: 但是,我们可能不知道的值,尽管我们知道…

本文汇编了一些机器学习领域的框架.库以及软件(按编程语言排序). 1. C++ 1.1 计算机视觉 CCV —基于C语言/提供缓存/核心的机器视觉库,新颖的机器视觉库 OpenCV—它提供C++, C, Python, Java 以及 MATLAB接口,并支持Windows, Linux, Android and Mac OS操作系统. 1.2 机器学习 MLPack DLib ecogg shark 2. Closure Closure Toolbox—Clojure语言库与工具的分类目录 3…

1    Unsupervised Learning 1.1    k-means clustering algorithm 1.1.1    算法思想 1.1.2    k-means的不足之处 1.1.3    如何选择K值 1.1.4    Spark MLlib 实现 k-means 算法 1.2    Mixture of Gaussians and the EM algorithm 1.3    The EM Algorithm 1.4    Principal Components…

Machine Learning Algorithms Study Notes 高雪松 @雪松Cedro Microsoft MVP 目 录 1    Introduction    1 1.1    What is Machine Learning    1 1.2    学习心得和笔记的框架    1 2    Supervised Learning    3 2.1    Perceptron Learning Algorithm (PLA)    3 2.1.1    PLA -- "知…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…

from:http://analyticsbot.ml/2016/10/machine-learning-pre-processing-features/ Machine Learning : Pre-processing features October 21, 2016 I am participating in this Kaggle competition. It is a prediction problem contest. The problem statement is: How…

What: 就是将统计学算法作为理论,计算机作为工具,解决问题.statistic Algorithm. How: 如何成为菜鸟一枚? http://www.quora.com/How-can-a-beginner-train-for-machine-learning-contests 链接内容总结: "学习任何一门学科,framework是必不可少的东西.没有framework的东西,那是研究." -- Jason Hawk One thing is for sure; you ca…

Why The Golden Age Of Machine Learning is Just Beginning Even though the buzz around neural networks, artificial intelligence, and machine learning has been relatively recent, as many know, there is nothing new about any of these methods. If so many…

Practical Machine Learning For The Uninitiated Last fall when I took on ShippingEasy's machine learning problem, I had no practical experience in the field. Getting such a task put on my plate was somewhat terrifying, and even more so as we started t…

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室 Jurgen Schmidhuber 写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从 1940 年开始讲起,到…

from: http://www.erogol.com/broad-view-machine-learning-libraries/ http://www.slideshare.net/VincenzoLomonaco/deep-learning-libraries-and-rst-experiments-with-theano FEBRUARY 6, 2014 EREN 1 COMMENT Especially, with the advent of many different and in…

Chapter 1 Introduction 1.1 What Is Machine Learning? To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algori…

A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee on September 9, 2016 in XGBoost 0 0 0 0   Gradient boosting is one of the most powerful techniques for building predictive models. In this post you will d…

Machine Learning for Developers Most developers these days have heard of machine learning, but when trying to find an 'easy' way into this technique, most people find themselves getting scared off by the abstractness of the concept of Machine Learnin…

What is machine learning? One area of technology that is helping improve the services that we use on our smartphones, and on the web, is machine learning. Sometimes, the terms machine learning and artificial intelligence get used as synonyms, especia…

17 Great Machine Learning Libraries 08 October 2013 After wonderful feedback on my previous post on Scikit-learn from the guys at /r/MachineLearning, I decided to collect the list of machine learning libraries into this seperate note. Let me know if…

A Statistical View of Deep Learning (III): Memory and Kernels Memory, the ways in which we remember and recall past experiences and data to reason about future events, is a term used frequently in current literature. All models in machine learning co…