Retour à Mathematics for Machine Learning: PCA

4.0

1,050 notes

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217 avis

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge.
The lectures, examples and exercises require:
1. Some ability of abstract thinking
2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis)
3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization)
4. Basic knowledge in python programming and numpy
Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

Jul 17, 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

May 01, 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

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par Faruk Y

•Sep 22, 2019

Lectures and programming assignments were selected nicely to teach the math of PCA

par Vo T T

•Sep 19, 2019

This course is very helpful for me to understand Math for ML. Thank you Professors at Imperial College London so much!

par María J S G

•Aug 29, 2019

Very good 3 courses for those of us who are beginners in Machine Learning and IA! However I miss a whole course, perhaps the first one of then four, teaching us what we need to know about python, numpy and plotting.

par Lintao D

•Sep 24, 2019

Very Good Course

par Sameen N

•Sep 06, 2019

Amazing course and provides basic introduction for the PCA. Need for programming help in this course.

par Lahiru D

•Sep 16, 2019

Great course. Assignments are tough and challenging.

par Cesar A P C J

•Dec 23, 2018

Good content, just need to fix the assignments' platform.

par Changxin W

•Jan 28, 2019

Many errors of homework

par Lafite

•Feb 04, 2019

编程练习的质量不够高，不管是编程练习本身的代码逻辑、注释、练习的质量还是在答疑区课程组的答疑都不能尽如人意，对于编程练习并不很满意

par Ronald T B

•Jan 21, 2019

it is very challenging course, of course you will complain at first on how lack the programming explanation is given. However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.

par Mark R

•Jan 22, 2019

Good, short, overview of PCA

par paulo

•Feb 11, 2019

great material but explanation are a little bit messy

par Camilo J

•Mar 01, 2019

Great capstone for the three-class Mathematics for Machine Learning series. Assignments were way harder and programming debugging skills had to be appropiate in order to finish the class.

par Shraavan S

•Mar 04, 2019

Programming assignments are a little difficult. Background knowledge of Python is recommended for this course.

par Thorben S

•Mar 08, 2019

I would have liked to be introduced to the topic on a higher level first - and then, step by step, an introduction of the math to solve specific problems in the progress. That would be a perfect approach, especially for data scientists who just want to understand the underlying math for such a widely used technique.

par Joshua C B A

•Mar 11, 2019

Very good course. I liked every single video and exercise. I feel that the programming assignments were a bit more challenging and sometimes I was not too sure of what I was doing. I am not a professional in handling Python, so I had to surf online finding the commands to be able to build the simplest code possible. Other than that, it was enjoyable.

par Jonathan F

•Mar 17, 2019

This course is way harder than the first two. The maths itself is more difficult. The Python parts are a lot more challenging because they require a good understanding of the way Numpy handles vectors and matrices. But the end result is good and it is worthwhile!

par kerryliu

•Jul 30, 2018

still have room for improvement since lots of stuffs can be discussed more in detail.

par Jiaqi W

•Aug 13, 2018

Coding assignment is hard for people who are not familiar with numpy. Would appreciate some material at least going over the basis.

par Nguyen D P

•Oct 17, 2018

That's a great online courses can help people have enough background to break into Machine Learning or Data science

par Sharon P

•Sep 25, 2018

Mathematically challenging, but satisfying in the end.

par SUJITH V

•Sep 28, 2018

This is a great course. It covers the topic in good amount of detail. I have enjoyed this course a lot and it also made me think deeper at a lot of places. I am motivated to go and do more work on related topics now.

par Manuel I

•Jul 07, 2018

Overall the hardest of the specialization, a though one but great to make sense of all the maths learned so far.

par Timo K

•Apr 10, 2018

Not quite as good as the other two courses of the same specialization. Even though the instructor seems immensely knowledgeable he could work on delivering the material (which is more abstract than before to his credit) in a clearer manner.

The programming assignments are great albeit a bit hard to troubleshoot at times. All in all still a great course.

par Ananthesh J S

•Jun 16, 2018

The PCA derivation part requires more elaborate explanation so that we can understand the concept more intuitively.

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