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机器学习基石第一讲笔记
近来觉得机器学习愈发重要。有人推荐了台大林轩田老师讲授的“机器学习基石”，感觉林老师讲得生动清楚，是很好的课程，值得一学。
第一讲介绍了机器学习是什么，使用机器学习的条件，机器学习的模型是怎样的。
1. 机器学习是一种处理复杂系统的方法，这里老师举了4个例子：
(1) when human cannot program the system manually --navigating on Mars
(2) when human cannot ‘define the solution‘ easily --speech/visual recognition
(3) when needing rapid decisions that humans cannot do --high-frequency trading
(4) when needing to be user-oriented in a massive scale --consumer-targeted marketing
(2) when human cannot ‘define the solution‘ easily --speech/visual recognition
(3) when needing rapid decisions that humans cannot do --high-frequency trading
(4) when needing to be user-oriented in a massive scale --consumer-targeted marketing
2. 机器学习的实质：improving some performance measure with experience computed from data
可以使用的条件： 
(1) exists some ‘underlying pattern‘ to be learned --so ‘performance measure‘ can be improved
(2) but no programmable (easy) definition --so ‘ML‘ is needed
(3) somehow there is data about the pattern --so ML has some ‘inputs‘ to learn from
(2) but no programmable (easy) definition --so ‘ML‘ is needed
(3) somehow there is data about the pattern --so ML has some ‘inputs‘ to learn from
3. 机器学习的模型：从训练数据D出发，用某种算法A从假设集合H中找出最接近目标f的假设g。
机器学习基石第一讲笔记
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