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What's ahead in the specialization

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机器学习基础：案例研究

华盛顿大学

4.6（8,666 个评分） | 200K 名学生已注册

课程 1（共 4 门，机器学习专项课程）

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，

查看授课大纲

您将学习的技能

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

审阅

4.6（8,666 个评分）

5 stars
6,312 ratings
4 stars
1,856 ratings
3 stars
324 ratings
2 stars
85 ratings
1 star
89 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.

从本节课中

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

教学方

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

脚本

[MUSIC]

Okay, so now that you guys are psyched about the future machine running,

let's talk about what we're gonna cover in the specialization.

Okay, so in the regression course, now that you guys know what regression is,

we're gonna go through a lot more of the details on different formulations

for models.

For example, how to cope with lots of features,

something that we eluded to in this course.

And we're also gonna talk about, in great detail, algorithms for

fitting these models.

So different optimization algorithms, remembering now that we've seen that

there's this cost we can talk about, residual sum of squares and minimizing it.

We're gonna talk about different optimization algorithms like gradient

descent and coordinate descent for actually doing this optimization.

And then, through this case study in predicting house prices,

we're gonna cover a lot of concepts that are really foundational to machine

learning in many different areas.

And some of these include how do we think about measuring cost.

How do we think about choosing between models and

dealing with overfitting of our model.

So we're gonna explore this in this context, but again, these ideas generalize

well beyond regression and well beyond predicting house prices.

Then when we get to the classification course,

we're gonna talk about specific examples of linear classifiers.

We're also gonna talk about methods of how to scale up to using lots and

lots of features and

creating classifiers in this very high dimensional feature representation.

And again, we're gonna talk about algorithms for

performing these types of classifications.

Specifically, looking at an optimization algorithm that allows us to scale up to

really, really large data sets.

And we're also gonna talk about this idea of how

we can think about blending different models using something called boosting.

And again, we'll look at many different concepts.

And one that I think is really interesting is how to do something called

online learning where we get data that just continually streams in and

we like to make our inferences continually as we get that data.

Then when we get to clustering and retrieval, again,

we've gone through the foundational ideas of what does it mean to do clustering and

what is our document retrieval task.

But we're gonna step it up even more where now, for example,

when we think about clustering, a document might not just be about sports or

world news or science or entertainment.

Maybe, a document has some mixture of different topics.

We can very easily think about a document that's about both finance and world news.

And so we're gonna think about how we model

this more complicated structure that might be present in our data.

And for the algorithmic side of things, we're gonna look at very efficient

ways for searching over data when we're doing our retrieval tasks.

And lots of different algorithms for

doing the types of clustering models that we're talking about.

And in terms of really important concepts that we're gonna cover in this course,

one thing is thinking about how to scale up doing the clustering to a really,

really massive collections of documents using something called map-reduce.

Next, we're gonna turn to our Recommender Systems and

Dimensionality Reduction course.

And here, beyond the types of collaborative filtering and

matrix factorization that we already talked about in this course.

We're also gonna talk about ways to take high dimensional data sets and

think about modelling in terms of some lower dimensional representation.

And so for that, we're gonna think about some algorithms for

doing this dimensionality reduction.

And we're also gonna talk about algorithms for

fitting the types of matrix factorization models that we described in this course.

And in this case, some of the important concepts we're gonna go through

especially when we're thinking about matrix factorization is how we think about

doing something like matrix completion.

And that's where we're filling in all the unknown squares,

if you remember that from this course.

And then a more general problem which is this cold-start problem where we,

in the case of our recommender systems, might have no information about a user or

a product and want to form those recommendations.

And finally, we're gonna get to the Capstone which is really,

as I hope you guys have got in a sense, gonna be very, very cool.

And now that you've gone through this course, you understand some of

the concepts that we talked about in terms of what makes up this Capstone.

In particular, we're gonna look at a recommender system

that combines ideas of doing text sentiment analysis with important

ideas from computer vision in terms of searching over different images.

And for doing this, we're gonna use deep learning, so

there's gonna be some really important and

more detailed information about deep learning presented in the Capstone.

So please get to that point.

It's really gonna be cool and very important and all of this

is gonna allow you to build this really intelligent web application and

deploy it and do things that impress,

not just your friends and family, but also potential employers.

So of course that's a potential bonus to some of you out there.

But it's really gonna be a lot of fun.

So we hope you get to that point and enjoy the Capstone project.

[MUSIC]

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