机器学习无处不在。
Machine learning is ubiquitous.
从医学诊断、语音、手写识别到自动交易和电影推荐，机器学习技术正被用于在每天每个时刻做出关键的商业和生活决策。
From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day.
每个机器学习问题都是独特的，因此管理原始数据、识别影响模型的关键特征、训练多个模型以及执行模型评估可能具有挑战性。
Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
本课程主要内容包括：
在MATLAB中访问、探索、分析和可视化输入数据
Accessing, exploring, analyzing, and visualizing data in MATLAB
使用“分类学习器”应用程序及“统计和机器学习工具箱”中的函数来执行常见的机器学习任务，例如：
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特征选择和特征转换
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指定交叉验证方案
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训练一系列分类模型，包括支持向量机（SVM）、boosted与bagged决策树、k近邻和判别分析
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使用混淆矩阵和ROC曲线执行模型评估和模型比较，以帮助为数据选择最佳模型
Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox® to perform common machine learning tasks such as:
o Feature selection and feature transformation
o Specifying cross-validation schemes
o Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
o Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data
将训练好的模型集成到诸如计算机视觉、信号处理和数据分析等应用中。
Integrating trained models into applications such as computer vision, signal processing, and data analytics.
与本DEMO相关的视频讲座网址：
http://www.mathworks.com/videos/machine-learning-with-matlab-100694.html
源码下载地址：
http://page5.dfpan.com/fs/2l5c8j427211f2b9160/
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