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Appetite Whetting: Bad Science

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Practical Predictive Analytics: Models and Methods

ワシントン大学（University of Washington）

4.1 (289 件の評価) | 30K在籍する学生

Data Science at Scale 専門講座における 4 の 2 コース

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection

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学習するスキル

Random Forest, Predictive Analytics, Machine Learning, R Programming

レビュー

4.1 (289 件の評価)

5 stars
131 ratings
4 stars
98 ratings
3 stars
31 ratings
2 stars
17 ratings
1 star
12 ratings

PV

Nov 12, 2015

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

CC

Jul 20, 2016

Nive that the course covered a broad range of topics.\n\nAnd good to get pushed to do some kaggle competition and peer review.

レッスンから

Practical Statistical Inference

Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.

Appetite Whetting: Bad Science5:09

Hypothesis Testing5:26

Significance Tests and P-Values3:57

講師

Bill Howe
Director of Research

字幕

[MUSIC]

All right so we're starting a new week, starting a new section of the course.

Where are we?

We've talked about what I, in this slide, are calling informatics.

So the management, manipulation, integration of data.

And we had some emphasis on scale.

And we had some emphasis on specific tools.

Okay, so now we're moving into what I'll call analytics.

And so we're gonna talk about statistical estimation and prediction.

And one of the points I wanna make is that this builds on the informatics,

in that the things you're gonna learn, you can implement using the tools before.

And we already saw a piece of this where we did sorta matrix multiplication in

various tools and so on.

Okay, the other point, if you remember,

we made early on was that 80% of what people think of as analytics

really boils down to the ability to do sums and averages.

And so we'll see a little bit of that in here.

Where maybe understanding the problem and

understanding the solution is hard or easy depending on maybe your background.

But as far as implementing it, it's not too bad.

Okay, and then we move onto visualization in a couple of weeks.

All right, so to get started on this.

I wanna call your attention to this article in 2010 from the New Yorker,

with the caveat that this is far from a research article.

And in fact, a lot of what the article has to say I'm not

sure I'd recommend taking to heart.

But the topic that they bring up is that, the title is here is The Truth Wears off.

And what they're exploring is this notion that statistical results

in the sciences seem to have gotten weaker over time.

And so John Davis is a researcher at the University of

Illinois who does work on antidepressants.

Is quoted and is discussed as talking about how a forthcoming

analysis demonstrating the efficacy of antidepressants has gone down as much as

threefold in recent decades.

So this is that the effectiveness

as measured by clinical trials of these antidepressants has gotten a lot weaker.

The article also talked about Anders Moller who studied barn swallows,

and discovered that the females were more likely to mate with

males that had long symmetrical feathers.

And his findings sort of relied on precise measurements of the symmetry.

And so this was a pretty significant discovery but

over the course of the next five or six years, the effect size as

discovered by himself and other researchers shrank by 80%.

Okay, a lot of the article talks about Jonathan Schooler in 1990,

who made a discovery of an effect that he called verbal overshadowing.

Which was counterintuitive because it showed that people

who are asked to describe a face using English that they've

seen were actually less likely to remember it than those who had just seen the face.

And so talking about the face somehow overshadowed the effect of

just seeing it alone.

Okay, and once again, this effect seemed to get weaker over time and

became increasingly difficult to measure, including by Jonathan Schooler himself.

And in fact he's quoted as saying this frustrated him.

He was having trouble replicating it.

And then they also bring up someone who's a little less respected.

Well, a little, [LAUGH] a fair amount less respected in the scientific community as

an historical example.

And is the person who actually coined the term, the decline effect,

which is brought up over and over again in the article.

In the 1930s, Joseph Rhine tested individuals with these card guessing

experiments in attempt to measure the effect of extrasensory perception or ESP.

And he's the one who actually coined that term.

He had a few students that achieved multiple streaks of very low probability.

Many, many cards in a row that they guessed right and so on.

But there was a decline effect and in the same.

Candidates, the same participants couldn't match their earlier performance.

And so

the article touches on what Is essentially the correct explanation of this effect.

And it also, sort of, brings up the possibility of a bunch

of quasi-mystical, incorrect explanations of this, at least in my opinion.

So I wanna tell you about, in the next couple of segments,

in the next few segments I wanna sort of explore this as a test case for

statistics and statistical estimation.

And I wanna use this as a vehicle to introduce the fundamental concepts of

statistics.

And also some of the somewhat more advanced concepts in statistics,

especially as they relate to big data.

[MUSIC]

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