Welcome to the final video of this Machine Learning class.
We've been through a lot of different videos together.
In this video I would like to just quickly
summarize the main topics of this course
and then say a few words at the end and that
will wrap up the class.
So what have we done?
In this class we spent a lot of time talking about supervised learning algorithms
like linear regression, logistic regression, neural networks, SVMs.
for problems where you have labelled data and labelled examples
like x(i), y(i)
And we also spent quite a lot of time talking about unsupervised learning
like K-means clustering,
Principal Components Analysis for dimensionality reduction
and Anomaly Detection algorithms for when you have only
unlabelled data x(i)
Although Anomaly Detection can also use some labelled data
to evaluate the algorithm.
We also spent some time talking about special applications
or special topics like Recommender Systems
and large scale machine learning systems
including parallelized and rapid-use systems
as well as some special applications like
sliding windows object classification for computer vision.
And finally we also spent a lot of time talking about different aspects
of, sort of, advice on building a machine learning system.
And this involved both trying to understand
what is it that makes a machine learning algorithm
work or not work.
So we talked about things like bias and variance,
and how regularization can help with some variance problems.
And we also spent a little bit of time talking about
this question of how to decide what to work on next.
So, how to prioritize how you spend your time
when you're developing a machine learning system.
So we talked about evaluation of learning algorithms,
evaluation metrics like precision recall, F1 score
as well as practical aspects of evaluation
like the training, cross-validation and test sets.
And we also spent a lot of time talking about
debugging learning algorithms and making sure
the learning algorithm is working.
So we talked about diagnostics
like learning curves and also talked about things like
error analysis and ceiling analysis.
And so all of these were different tools for helping you to decide
what to do next and how to spend your valuable
time when you're developing a machine learning system.
And in addition to having the tools of machine learning at your disposal
so knowing the tools of machine learning like
supervised learning and unsupervised learning and so on,
I hope that you now not only have the tools,
but that you know how to apply these tools really well
to build powerful machine learning systems.
So, that's it.
Those were the topics of this class
and if you worked all the way through this course
you should now consider yourself
an expert in machine learning.
As you know, machine learning is a technology
that's having huge impact on science, technology and industry.
And you're now well qualified to use these tools
of machine learning to great effect.
I hope that many of you in this class
will find ways to use machine learning
to build cool systems and cool applications
and cool products.
And I hope that you find ways
to use machine learning not only
to make your life better but maybe someday
to use it to make many other people's life better as well.
I also wanted to let you know that this class has been great fun for me to teach.
So, thank you for that.
And before wrapping up,
there's just one last thing I wanted to say.
Which is that: It was maybe not so long ago,
that I was a student myself.
And even today, you know, I still try to take different courses
when I have time to try to learn new things.
And so I know how time-consuming it is
to learn this stuff.
I know that you're probably a busy person
with many, many other things going on in your life.
And so the fact that you still found
the time or took the time to watch these videos
and, you know, many of these videos just went on
for hours, right?
And the fact many of you took the time
to go through the review questions
and that many of you took the time
to work through the programming exercises.
And these were long and complicate programming exercises.
I wanted to say thank you for that.
And I know that many of you have worked hard on this class
and that many of you have put a lot of time into this class,
that many of you have put a lot of yourselves into this class.
So I hope that you also got a lot of out this class.
And I wanted to say:
Thank you very much for having been a student in this class.