Explore
For Enterprise
Join for Free
Log In

艺术与人文
商务
计算机科学
数据科学
信息技术
健康
数学和逻辑
个人发展
物理科学与工程
社会科学
语言学习
學位和證書

瀏覽 Coursera 的全部課程

瀏覽
搜索
企業版
登錄
免費加入

Open challenges in ML

Loading...

机器学习基础：案例研究

华盛顿大学

4.6（8,561 個評分） | 190K 名學生已註冊

課程 1（共 4 門，机器学习專項課程）

查看授課大綱

你是否好奇数据可以告诉你什么？你是否想在关于机器学习促进商业的核心方式上有深层次的理解？你是否想能同专家们讨论关于回归，分类，深度学习以及推荐系统的一切？在这门课上，你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候，

您將學習的技能

Python Programming, Machine Learning Concepts, Machine Learning, Deep Learning

審閱

4.6（8,561 個評分）

5 stars
6,240 ratings
4 stars
1,828 ratings
3 stars
322 ratings
2 stars
83 ratings
1 star
88 ratings

DS

Sep 28, 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

GG

Jun 05, 2017

This course is very helpful for people who are novice in machine learning. The course uses Graphlab Create which is different from scikit or R-libraries, but the tool(Graphlab) is excellent to use.

從本節課中

Closing Remarks

In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment.<p>We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning is heading. We conclude with an overview of what's in store for you in the rest of the specialization, and the amazing intelligent applications that are ahead for us as we evolve machine learning.

Open challenges in ML8:40

Where is ML going?6:29

What's ahead in the specialization5:38

教學方

Carlos Guestrin
Amazon Professor of Machine Learning
Emily Fox
Amazon Professor of Machine Learning

[MUSIC]

Okay, so in this course,

we've talked about lots of different machine learning methods and

lots of applications where these types of methods can be very impactful.

But of course there a lots of open challenges that still remain in

machine learning.

So let's discuss some of them.

One is the fact we often have a choice of which model to use.

So for example,

when we talked about recommending products we said we can use a classification model.

Where we take features of the user and the product, pass it through this classifier

to say whether yes or no the person will like or not like this product.

But then we also talked about using matrix factorization where we're gonna learn

features about users and products and use that to recommend products to the users.

And then we also talked about featurized matrix factorization, combining these two

ideas the list often of possible models we can consider for a task is very large.

So typically, this leaves the practitioner very perplexed.

Which model should I use and searching over this

set of possible choices is still an open challenge in machine learning.

Another really important challenge that we're often faced with is

how do we represent our data?

So for example, when we talked about our document modeling,

our document retrieval task, we said, well, we could use just raw word counts,

or we also talked about we could normalize the vectors.

We could use things like tf-idf to account for

very popular words, and to more emphasize the important words in the document.

But honestly, there are lots of different variants of tf-idf,

we just provided one example of doing this.

You could also think about using BiTrams, and trigrams, and there are lots and lots

of ways we can think about representing the words that appear in a document.

That's, our, our data set of interest that we'd like to represent.

But that's just for a document.

Then maybe we have images.

How do we represent an image?

We've talked about some ways.

We'll talk about others, but there's lots of challenges with that.

Then maybe you have data that's really network based,

so things like from Facebook.

So you can have very complicated data structures and from very,

very different, diverse data sets.

We wanna be able to use the types of methods we've described.

So, how we represent our data, of course, is gonna have

significant impact on the types of inferences that we make on that data.

So, this is a really really important problem and there's no one method for

choosing the right representation of your data.

One of the other really important and really significant challenges we're faced

with in machine learning these days is how to scale up in multiple dimensions.

So one aspect of this is the fact that data is getting bigger and bigger.

So this is something that's been talked about extensively in the media.

So let's just describe a few situations in which we're faced with

a growing amount of data.

One is the fact that there's just a large number of different platforms out there

for social networking, collecting data via crowdsourcing,

and different things like this, sharing your photos, your videos.

And reviewing restaurants and the list of possible ways in which you can now

go online and give data to the world is growing.

And the amount of people doing this and

providing data is just growing at this huge huge rate.

So we have lots of new data sources available to us.

And in addition, the way we think about buying products, we no longer often just

go to a store and have some hand written record of what, product was purchased.

We now have, vendors like Amazon who have these huge online

marketplaces and collect data about different products and

customers and different purchases being made and lots and

lots of data from different sources like this.

And beyond these types of websites there's also a lot

of devices that we can now wear.

So there are these wearable devices I can now wear

a watch that monitors all the activities I'm doing, how I'm sleeping at night.

I can wear glasses that record everything that I'm seeing.

I can also talk about the Internet of Things,

which is just lots of connected devices and

lots of different sources of information communicating with one another.

So these are just some of the areas in which we're seeing lots and

lots of new data sources, but of course that's not exhaustive.

We can also talk about things like medical records.

Again, no longer do you go into your doctor's office and

just have them write notes by hand that gets put in some file.

Often, they're taking electronic health records and

these are now communicating across systems and we have lots and

lots of electronic health records that, it's just a source of data

to be parsed and understood and used to innovate in medicine.

So lots of new datasets, which is exciting.

We can learn a lot about how people operate about our bodies, about how people

purchase and make friends and how they go about their day to day activities but of

course we need to have methods that scale to analyze these types of data sets and

also to the unique structure of data that's presented in these data sets, and

the noisy structure, and the list of challenges is really extensive.

This is one of the very big challenges in machine learning,

is how to deal with this big data.

And simultaneously, to data being really large,

we're also faced with the challenge with the fact that the models that

we're using to analyze these increasingly complex data sets.

Are also growing, so the models themselves are becoming bigger and

more complicated in order to extract information from these ever growingly.

I don't know if that's a word, but you get my point.

These very intricate data sources and very large data sources.

So just as an example, when we talked about clustering we talked about this,

application where, you have,

recordings of brain activity taken over time, so, this is just one,

quick example of, a model that was used to analyze this type of data set,

and without going into the details of what's shown here on this slide.

Just realize that there are lots of circles and lots of arrows, and

what that means is that this is a really complicated big, big model.

So you might think, okay, well data's getting bigger, models are getting bigger,

but that's okay because processors are getting faster.

Well, that was the story for a while.

We were seeing this exponentially increasing rate of increased

speed for our processors.

But that stopped about a decade ago.

And now what we're seeing,

is really very marginal increase in the speed of an individual processor.

So instead, we have to think about new ways to scale up.

And the typical thing that we're leveraging these days

is collections of processors.

And there are different architectures, that we have.

We have things like GPUs and multicore and clusters and cloud

computing resources and really, really fancy and expensive super computers.

So that's great.

Those are really, really powerful or

potentially powerful computing resources that we have.

But a question is how do we use these in machine learning?

And in machine learning we have a number of challenges we're faced with.

One is the fact that taking our machine learning out of those and

thinking about how to distribute them across these different processors and

run everything we want to run in a coherent way, is very challenging.

Another challenge is how do we distribute the data across these different machines

and how do we do all of this in a way that is

very tolerant to different failures we can have of the individual machines.

So these represent a number of challenges that we are facing in machine learning.

And a lot of lot exciting research is coming out to start addressing these

problems.

[MUSIC]

探索我們的目錄

免費加入並獲得個性化推薦、更新和優惠。

Coursera 致力於普及全世界最好的教育，它與全球一流大學和機構合作提供在線課程。

© 2019 Coursera Inc. 保留所有權利。

通過 App Store 下載

通過 Google Play 獲取

Coursera
關於
管理團隊
工作機會
目錄
證書
MasterTrack™ 證書
學位
企業版
政府版

社區
Learners
Partners
Developers
Beta Testers
Translators

連接
博客
Facebook
領英
Twitter
Instagram
YouTube
技術博客

更多
條款
隱私
幫助
內容訪問
媒體
聯繫我們
目錄
附屬公司