<Machine Learning>系列学习笔记 第一周 第一部分 Introduction The definition of machine learning (1)older, informal definition--Arthur Samuel--"the field of study that gives computers the ability to learn without being explicitly programmed." (2)modern d…

1.Introduction 1.1 Example        - Database mining        Large datasets from growth of automation/web.        E.g., Web click data, medical records, biology, engineering        - Applications can't program by hand.        E.g., Atonomous helicopter…

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 Note Introduction Introduction What is Machine Learning? Two definitions of Machine Learning are offered. Arthur Samuel described it as:"the filed of study that gives computers the ability to learn without being explicitly programmed…

Step 1: Basic Python Skills install Anacondaincluding numpy, scikit-learn, and matplotlib Step 2: Foundational Machine Learning Skills Unofficial Andrew Ng course notes Tom Mitchell Machine Learning Lectures Step 3: Scientific Python Packages Overvie…

The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and this week's videos explain the 'backprogagation' algorithm for training these models. In this week's progr…

Matrix 定义及基本运算 Transposing To "transpose" a matrix, swap the rows and columns. We put a "T" in the top right-hand corner to mean transpose: Inverse of matrix The Inverse of A is A-1 only when: A × A-1 = A-1 × A = I Sometimes there is n…

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…

Support Vector Machine (large margin classifiers ) 1. cost function and hypothesis 下面那个紫色线就是SVM 的cost function       2. SVM 的数学解释                           3. SVM with kernel 我的理解是 kernel 的作用就是把低维度的 x 转化成高维的 f, 然后就好分类了   note: 上图就是一个2维(x1, x2)变3维(f1,…

了解LR的同学们都知道,LR采用了最小化交叉熵或者最大化似然估计函数来作为Cost Function,那有个很有意思的问题来了,为什么我们不用更加简单熟悉的最小化平方误差函数(MSE)呢? 我个人理解主要有三个原因: MSE的假设是高斯分布,交叉熵的假设是伯努利分布,而逻辑回归采用的就是伯努利分布: MSE会导致代价函数$J(\theta)$非凸,这会存在很多局部最优解,而我们更想要代价函数是凸函数: MSE相对于交叉熵而言会加重梯度弥散. 这里着重讨论下后边两条原因. 代价函数为什么要为凸函数…