返回到 Mathematics for Machine Learning: PCA

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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!

篩選依據：

創建者 Вернер А И

•Mar 18, 2018

Very tough course because of the programming assignments. Material was sometimes taught in a non-clear and deceiving way, e.g. covariance matrix of a dataset. Nevertheless, the course is good and covers lots of important details.

創建者 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.

創建者 João M G

•Aug 14, 2019

The course was great till the final week. The lectures did not explain very well the concepts and the assignment was poorly designed. It's a shame because I've loved the more rigorous way of this final course.

創建者 ranzhang

•Aug 29, 2019

I think it's really a hard lesson for me, but I've also learn a lot, thanks a lot for the teacher and coursera. Some Programming test take too long to execute, and there are some errors in it. just be careful

創建者 Suyog P

•Sep 02, 2019

Finally understood basic intuition of PCA, never got perfect resource before. However, there was a sharp change in terms of course delivery than the previous two courses of this specialization. So, heads up.

創建者 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.

創建者 Faisal

•May 28, 2020

Course content is interesting and well planned, Can be improved by making it Simpler for Students as it was more technical than the other 2 courses of the Specialization.

創建者 Abhishek P

•Sep 09, 2019

Course content tackles a difficult topic well. Only flaw is that programming assignments are poorly designed in some places and are quite difficult to pick up at times.

創建者 Liang S

•Jul 09, 2018

Teaching pacing is good, and clear in explanation. It will be good if there are some examples about how we should apply all these theories to some real problems.

創建者 Mohamed B

•Oct 27, 2019

I learned a lot in this course, though the last week was somehow hurried and the lecturer didn't spend enough time to piece the whole stuff together.

創建者 Rok Z

•Feb 05, 2020

A different course than the previous 2.

Much harder - as you have to actually know some Python tricks.

But I guess it's the same in a real world.

創建者 Jordan V

•Aug 23, 2019

Course addresses important subject, but I worth like to have more in-depth explanation of the mathematics by the instructors and more examples.

創建者 Kevin G

•Jan 14, 2020

Felt like explanations in this course were a bit confusing, but otherwise, it was a very interesting course. Thank you so much for doing this.

創建者 Helena S

•Feb 28, 2020

The final Notebook contains some errors (Xbar instead of X, passed as an argument). Otherwise a very well organized course. Thanks a lot!

創建者 Giri G

•Jun 07, 2019

This was a very hard course for me. But I think the instructor has done the best possible he can with presenting and explaining the course

創建者 Christine 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.

創建者 Shaiman Z S

•Apr 30, 2020

Please change courese material for PCA. It is very un-understandable and assignments are also very tugh as per what is taught.

創建者 Hilmi E

•Apr 20, 2020

Careful, step-by-step construction of the PCA algorithm with practical exercises and coding assignments.. Very well done...

創建者 Voravich C

•Oct 21, 2019

The course level is very difficult and I think having four week course is not enough to understand the math behind PCA

創建者 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

創建者 Ananthesh J S

•Jun 16, 2018

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

創建者 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.

創建者 Shraavan S

•Mar 04, 2019

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

創建者 Andrew D

•Jun 02, 2019

Very difficult course, make sure to do the prereq courses first and understand everything from those courses.

創建者 Ibon U E

•Jan 07, 2020

The derivations of some concepts have been more vague compared to other courses in this specialization.