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2010年11月03日(水)12時12分 
ネットで情報科学系の技術をサーベイをしていたところ、次のような動画を発見しました。



これはスタンフォード大学で行われている機械学習と人工知能の講義です。

大学のWebページを確認すると、何とトランスクリプト付き。これは格好の英語学習教材になり得るぞ、、、ということで、今後のブログネタとして、何度かに分けてこの講義の解説をしていきたいと思います。

動画は1時間以上もありますが、そのうち1枚のスライドを説明している4分間のスクリプトを取り出してみました。再生は該当箇所の[32:30]から開始するようにしてあります。
※スクリプトは大学のHPより転載したものです(This script was copied from Stanford engineering Web site.)

日本語での解説は次回以降ということで、まずはスクリプトを眺めてみてください。


[32:30]

So start by talking about what machine learning is. What is machine learning? Actually, can you read the text out there? Raise your hand if the text on the small screens is legible. Oh, okay, cool, mostly legible. Okay. So I'll just read it out.


So what is machine learning? Way back in about 1959, Arthur Samuel defined machine learning informally as the [inaudible] that gives computers to learn ・[inaudible] that gives computers the ability to learn without being explicitly programmed. So Arthur Samuel, so way back in the history of machine learning, actually did something very cool, which was he wrote a checkers program, which would play games of checkers against itself.


And so because a computer can play thousands of games against itself relatively quickly, Arthur Samuel had his program play thousands of games against itself, and over time it would start to learn to recognize patterns which led to wins and patterns which led to losses. So over time it learned things like that, "Gee, if I get a lot of pieces taken by the opponent, then I'm more likely to lose than win," or, "Gee, if I get my pieces into a certain position, then I'm especially likely to win rather than lose."


And so over time, Arthur Samuel had a checkers program that would actually learn to play checkers by learning what are the sort of board positions that tend to be associated with wins and what are the board positions that tend to be associated with losses. And way back around 1959, the amazing thing about this was that his program actually learned to play checkers much better than Arthur Samuel himself could.


So even today, there are some people that say, well, computers can't do anything that they're not explicitly programmed to. And Arthur Samuel's checkers program was maybe the first I think really convincing refutation of this claim. Namely, Arthur Samuel managed to write a checkers program that could play checkers much better than he personally could, and this is an instance of maybe computers learning to do things that they were not programmed explicitly to do.


Here's a more recent, a more modern, more formal definition of machine learning due to Tom Mitchell, who says that a well-posed learning problem is defined as follows: He says that a computer program is set to learn from an 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. Okay. So not only is it a definition, it even rhymes.


So, for example, in the case of checkers, the experience E that a program has would be the experience of playing lots of games of checkers against itself, say. The task T is the task of playing checkers, and the performance measure P will be something like the fraction of games it wins against a certain set of human opponents. And by this definition, we'll say that Arthur Samuel's checkers program has learned to play checkers, okay?


So as an overview of what we're going to do in this class, this class is sort of organized into four major sections. We're gonna talk about four major topics in this class, the first of which is supervised learning. So let me give you an example of that.

[36:30]
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