1-Introduction

Part 1:welcome

Examples:

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. ,Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.

Self-customizing programs

E.g. ,Amazon, Netflix(一家在线影片租赁提供商) product recommendations

Understanding human learning (brain, real AI).

Part 2:What’s Machine Learning

1.By Arthur Samuel :

Machine Learning : Field of study that gives computers the ability to learn without being explicitly programmed.

Example: checkers(国际象棋) learning program

2.By Tom Mitchell :

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.

Example: Email anti-spam Classify Program

3. Machine Learning Algorithms:

Supervised learning 有监督学习

Unsupervised learning 无监督学习

Others: Reinforcement learning(增强学习)、Recommender systems 推荐系统

Part 3: Supervised Learning

the “right answers” of * data set* were given.(数据集已经给出了分类，为每一个数据打了标签)

Regression（回归问题）: Predict continuous valued output

E.g. : Classification（分类问题）

Breast cancer prediction:malignant or benign(only one feature: Tumor size)

Two feature: Age and Tumor size,

….

infinite feature : Support Vector Machine

Part 4:Unsupervised Learning

Doesn’t have any labels or that all has the same label or really no labels.(只有数据，没有标签)

Clustering(聚类问题)

新闻分类、基因分类

处理器集群聚类、社会关系分析

Market segmentation、Astronomical data analysis

Coktail party problem

将混合的两种声音分离开来，用C++、Java等实现起来非常复杂，但是用Octave只需一行代码:

[W,s,v] = svd((repmat(sum(x.*x,1),size(x,1),1).*x)*x');