返回到 Mathematics for Machine Learning: PCA

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1,379 個評分

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305 條評論

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 Prime S

•Jun 24, 2018

Nicely explained. Could be further improved by adding some noted or sources of derivation of some expressions, like references to matrix calculus

創建者 J A M

•Mar 21, 2019

Solid conceptual explanations of PCA make this course stand out. The thorough review of this content is a must for any serious data researcher.

創建者 Moez B

•Nov 25, 2019

Excellent course. The fourth week material is the hardest for folks not comfortable with linear algebra and vectorization in numpy and scipy.

創建者 Hasan A

•Dec 31, 2018

What a great opportunity this course offers to learn from the best in this simplified manner. Thank you Coursera and Imperial College London!

創建者 Alexander H

•Jul 31, 2018

Highly informative course! Loved the depth of the material. Found this course content highly useful in my current project based on PCA.

創建者 Jason N

•Feb 20, 2020

A lot of reading beyond the video lectures was required for me and some explanations could be more clear. Overall, a great course.

創建者 UMAR T

•Mar 10, 2020

Excellent course it helps you understanding about linear algebra programming into real world examples by programming in python.

創建者 Dora J

•Feb 04, 2019

Great course including many useful refreshers on foundational concepts like inner products, projections, Lagrangian etc.

創建者 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!

創建者 David H

•Mar 21, 2019

It was challenging but worth it to enhance the mathematic skills for machine learning. Thanks for the awesome course.

創建者 Lee F

•Sep 28, 2018

This was the toughest of the three modules. It gave me a strong foundation to continue pusrsuing machine learning.

創建者 Krzysztof

•Aug 21, 2019

One of the most challenging course in my life - almost impossible without python and mathematics background.

創建者 Sameen N

•Sep 06, 2019

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

創建者 Brian H

•Feb 25, 2020

Great course. I appreciate the rigor and clear mathematical explanations provided by Dr. Deisenroth.

創建者 Natalya T

•Feb 25, 2019

exellent course! nice python wokring enviroment and very good explanation at each topic. thank you!

創建者 Aishik R C

•Jan 18, 2020

Excellent and to-the-point explanations, useful assignments to make the concepts etched in memory

創建者 Wei X

•Oct 16, 2018

concise and to the point. Might want to introduce a bit the technique of Lagrangin multiplier

創建者 Ripple S

•Mar 18, 2020

I learnt a lot from this course and now I think I am much more familiar with this algorithm.

創建者 Mohammad A M

•Nov 14, 2019

This course is also so helpful, and the lecturer is so predominant on what he taught.

創建者 Alfonso J

•Oct 20, 2019

Truly hardcore course if your are a noob in reduced order modelling. Very challenging

創建者 Arijit B

•Nov 05, 2019

Excellent course and extremely difficult one to grasp at one go. Regards Arijit Bose

創建者 ELINGUI P U

•May 26, 2018

Very hard to follow, but you need to do it to understand machine learning very well.

創建者 Greg E

•Jul 27, 2019

I have thoroughly enjoyed every course of this specialization. Thank you very much.

創建者 Faruk Y

•Sep 22, 2019

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

創建者 Lia L

•May 22, 2019

This was really difficoult, but I'm so proud for the completion of the course.