Here is the note for lecture three. the linear model Linear model is a basic and important model in machine learning. 1. input representation     The data we get usually needs some changes, most of them is the input data.      In linear model,       …

Here is the note for lecture five. There will be several points  1. Training and Testing  Both of these are about data. Training is using the data to get a fine hypothesis, and testing is not. If we get a final hypothesis and want to test it, it turn…

Machine Learning and Data Mining Lecture 1 1. The learning problem - Outline     1.1 Example of machine learning Predicting how a viewer will rate a moive? 10% improvement = 1 million dollar prize The essence of machine learning: A pattern exists We…

How do you explain Machine Learning and Data Mining to non Computer Science people?   Pararth Shah, ML Enthusiast Answered Dec 22, 2012 · Featured on VentureBeat · Upvoted by Melissa Dalis, CS & Math major at Duke and Alberto Bietti, PhD student in m…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

machine learning(13) --Regularization:Regularized linear regression Gradient descent without regularization                    with regularization                     θ0与原来是的没有regularization的一样 θ1-n和原来相比会稍微变小(1-αλ⁄m)<1 Normal equation without regular…

Multiple Features 上一章中,hθ(x) = θ0 + θ1x,表示只有一个 feature.现在,有多个 features,所以 hθ(x) = θ0 + θ1x1 + θ2x2 + ... + θjxj.为了标记的方便,增加 x0 = 1 用向量表示 这里的 X 表示单行 Xi.如果是表示所有的 hθ(x),就会 X * θ(X 表示所有 x 的值) Gradient Descent For Multiple Features 也是同理,扩展到 j 个,就不再赘述. Grad…

这学期一直在跟进 Coursera上的 Machina Learning 公开课, 老师Andrew Ng是coursera的创始人之一,Machine Learning方面的大牛.这门课程对想要了解和初步掌握机器学习的人来说是不二的选择.这门课程涵盖了机器学习的一些基本概念和方法,同时这门课程的编程作业对于掌握这些概念和方法起到了巨大的作用. 课程地址 https://www.coursera.org/learn/machine-learning 笔记主要是简要记录下课程内容,以及MATLAB…

http://www.cs.cmu.edu/~avrim/courses.html Foundations of Data Science Avrim Blum, www.cs.cornell.edu/jeh/bookJan25_2016.pdf…

核心期刊排名查询 http://portal.core.edu.au/conf-ranks/ http://portal.core.edu.au/jnl-ranks/ 1.机器学习推荐会议 ICML——很难读懂,值得一读 NIPS ——主要看文章摘要 ECML 2.数据挖掘推荐会议: ICDE ICDM——略逊 CIKM KDD 3.IR推荐会议 SIGIR WWW 4.NLP推荐会议 ACL EMNLP EAC NNACL coling CCF推荐会议列表 expert system with…

一.Model representation(模型表示) 1.1 训练集 由训练样例(training example)组成的集合就是训练集(training set), 如下图所示, 其中(x,y)是一个训练样例, (x(i),y(i))是第 i个训练样例. 1.2 假设函数 使用某种学习算法对训练集的数据进行训练, 我们可以得到假设函数(Hypothesis Function), 如下图所示. 在房价的例子中,假设函数就是一个房价关于房子面积的函数.有了这个假设函数之后, 给定一个房子的面积…

单变量线性回归(Linear Regression with One Variable) 什么是线性回归?线性回归是利用数理统计中回归分析,来确定两种或两种以上变量间相互依赖的定量关系的一种统计分析方法(取自 百度百科). 例如:现在有一堆散乱的点,想找出一个一元一次方程来让这些点的分布误差最小(就是找出一条最合适的直线来贯穿这些点). 图中红色直线就是我们需要找的线.这条直线的表示为: y=ax+b.那么找出a.b这两个变量最合适的值就叫线性回归. 在图片中,蓝色的点用(xi,yi)来表示.m…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amoun…

https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning   How do I learn mathematics for machine learning? Promoted by Time Doctor Software for productivity tracking. Time tracking and productivity improvement software with screenshots…

8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset by Jason Brownlee on August 19, 2015 in Machine Learning Process Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy…

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…

About me In my spare time, I love learning new technologies and going to hackathons. Our hackathon project Pantrylogs using Artificial Intelligence was selected as one of the 10 Microsoft Imagine Cup UK finalists. I’m interested in learning more abou…

https://emerj.com/ai-sector-overviews/machine-learning-in-finance/ Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate histor…

原文 :https://medium.com/machine-learning-in-practice/roles-on-a-machine-learning-project-216903a6dc12 Machine learning is a technical process, but it starts and ends with people. The first step to structuring your machine learning project is to consid…

<Machine Learning in Action>-- 白话贝叶斯,"恰瓜群众"应该恰好瓜还是恰坏瓜 概率论,可以说是在机器学习当中扮演了一个非常重要的角色了.Taoye对概率论知识的掌握目前也还仅仅只是停留在本科期间所接触到的,而且还都已经忘了不少.快速的复习回顾一下之后,用来理解机器学习中的贝叶斯算法,还是足够的. 手撕机器学习系列文章目前已经更新了支持向量机SVM.决策树.K-近邻(KNN),现在我们来玩玩贝叶斯算法,其他机器学习系列文章可根据自己需求来食用(持…

整理自Andrew Ng的machine learning课程week6. 目录: Advice for applying machine learning (Decide what to do next) Debugging a learning algorithm machine learning diagnostic Evaluating a hypothesis Model selection and Train / validation / test set Bias and Vari…

一.原理和概念 1.回归 回归最简单的定义是,给出一个点集D,用一个函数去拟合这个点集.而且使得点集与拟合函数间的误差最小,假设这个函数曲线是一条直线,那就被称为线性回归:假设曲线是一条二次曲线,就被称为二次回归. 以下仅介绍线性回归的基本实现. 2.假设函数.误差.代价函数 参考  Machine Learning 学习笔记2 - linear regression with one variable(单变量线性回归) 最小化误差一般有两个方法:最小二乘法和梯度下降法 最小二乘法可以一步到位,…

Getting started with machine learning in Python Machine learning is a field that uses algorithms to learn from data and make predictions. Practically, this means that we can feed data into an algorithm, and use it to make predictions about what might…

How to handle Imbalanced Classification Problems in machine learning? from:https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/ Introduction If you have spent some time in machine learning and data science, you would have d…

说在前面:前几天,公众号不是给大家推送了第二篇关于决策树的文章嘛.阅读过的读者应该会发现,在最后排版已经有点乱套了.真的很抱歉,也不知道咋回事,到了后期Markdown格式文件的内容就解析出现问题了,似乎涉及到Latex就会多多少少排版错乱???暂时也没什么比较好的解决办法,如果有朋友知道的可以联系下Taoye,长时间用Markdown + Latex码文已成习惯了,关于机器学习文章的内容,更好的阅读体验,大家可以跳转至我在Cmd Markdown平台发布的内容,也可前往我的掘金主页,阅读体验都…

<Machine Learning in Action>-- 浅谈线性回归的那些事 手撕机器学习算法系列文章已经肝了不少,自我感觉质量都挺不错的.目前已经更新了支持向量机SVM.决策树.K-近邻(KNN).贝叶斯分类,读者可根据以下内容自行"充电"(持续更新中): <Machine Learning in Action>-- 剖析支持向量机,单手狂撕线性SVM: https://www.zybuluo.com/tianxingjian/note/1755051…

在手撕机器学习系列文章的上一篇,我们详细讲解了线性回归的问题,并且最后通过梯度下降算法拟合了一条直线,从而使得这条直线尽可能的切合数据样本集,已到达模型损失值最小的目的. 在本篇文章中,我们主要是手撕Logistic回归,这个在李航老师的<统计学习方法>一书中也叫做为逻辑斯谛回归.听到回归一词,有的读者可能会想,上一篇线性回归求解的是拟合问题,这篇文章手撕的是Logistic回归,会不会也是一个拟合问题?只不过使用到的算法原理不同而已,而求解的问题是一致的??? 其实不然,Logistic回归…

数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics)之间有什么关系? 本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答不出来,我在知乎和博客上查了查这个问题,发现还没有人写过比较详细和有说服力的对比…

本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答不出来,我在知乎和博客上查了查这个问题,发现还没有人写过比较详细和有说服力的对比和解释.那我根据以前读的书和论文,还有和与导师之间的交流,尝试着说一说这几者的区别吧,毕竟一个好的定义在未来的学习和交流中能够发挥很大的作用.同时补上数据科学和商业分析之间的关系.能力有限,如有疏漏,请包涵和指正. 导论…