返回到 Mathematics for Machine Learning: PCA

4.0

stars

1,271 個評分

•

271 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 Nicholas K

•Apr 28, 2018

It's a shame. There's lots of good material and I learned a lot. But a staggering amount of time was wasted figuring out gaps in the instructions - portions felt more like hazing than teaching.

創建者 Adrian C

•Sep 22, 2019

The derivatiion of the PCA in the last week can be broken into 2 weeks with different programming assignments to get a closer and more confident understanding of the PCA method.

創建者 Jean D D S

•Aug 31, 2019

I would ask the lecturer to go on more detail on the explanations and do (more) examples.

The lecturer tends to skip a few steps during calculations and demonstrations.

創建者 Wang Z

•Jul 08, 2018

The knowledge introduced in this course is really helpful. However, the programming assignments are very time consuming and not necessarily relevent

創建者 Iurii S

•Mar 26, 2018

Decent explanations of PCA idea, but assignments do not provide a clear feedback of what is wrong with the implementation util you get it right.

創建者 NEHAL J

•Apr 21, 2019

The course was highly challenging. I wish some of the explanations were detailed and the assignments had better instructions.

創建者 Ana P A

•Apr 22, 2019

The professor of other two a way better. This one skips some steps in some explanation that makes the tasks hard to do

創建者 Chuwei L

•Apr 05, 2019

worse than previous courses of machine learning specialization. Really confused me when introduced the inner products.

創建者 Jyh1003040

•Jul 09, 2018

Honestly this course is the one worthing attempting. However, last week's content is really messy and challenging.

創建者 Hsueh-han W

•Sep 20, 2019

many steps are not clear enough that I have to spend a lot of additional time to figure out the details.

創建者 Gurudu S R

•Sep 16, 2019

Tutor is not clear and concise on the concepts. Need more examples for Week 2 and Week 3.

創建者 Sagun P S

•Mar 14, 2019

Tough one if you are new to programming or doesn't have excellent understanding of Maths

創建者 Matan A

•Oct 20, 2019

The is a lot of gap from what the lecturer learn and what the assignments requires.

創建者 Yuxuan W

•Oct 05, 2018

Always spending much more time on coding than needed. Same result but no credit :(

創建者 Rafael C

•Dec 07, 2019

definitely one of the most catastrophic courses I've ever taken on Coursera...

創建者 Meraldo A

•May 08, 2018

The course content was good; however, it was not well explained at times.

創建者 Alexander

•Nov 06, 2019

Math for the sake of math. Too big jumps in calculations, too complex.

創建者 k v k

•Nov 30, 2018

its a good course to learn mathematics essential for machine learning

創建者 Rafael C

•Sep 24, 2019

The Classes didn't give the knowledge to solve the assignments.

創建者 Shuyu Z

•Oct 18, 2019

The videos and instructions for the assignment are not clear.

創建者 gaurav k

•Jul 03, 2019

More examples and visualization should be there to explain.

創建者 Malcolm M

•Mar 06, 2019

Far more challenging than the first two courses.

創建者 Sean W

•Nov 25, 2019

Notebook extremely buggy

創建者 Tobias T

•Jul 14, 2019

If you like traditional lectures, which you go into a math class then feel puzzled, then go for it. Otherwise, the contents of this course are simply going through the mathematics equations and definitions, which can easily be found in textbooks. Ironically, the previous two courses in this specialization used lots of graphics and animations to help you understand the maths (either in terms of equation-wise or intuitively), this course completely lacks this element.

創建者 Mark C

•Jul 31, 2018

Only on week 1 but this is already a disappointment compared to the first two classes in the Math for ML series which were excellent. Some content is presented too fast. Quiz questions are ambiguous. I already paid for the class so I will finish the content but not worry about passing quizzes and assignments. Had I known it would be like this I wouldn't have paid for it. Check out the other reviews and forum discussions to see what others think.