Mathematics for Machine Learning

There are some issues with the programming assignments and the lectures could do with some more practical examples.

I feel that the programming assignments were a bit more challenging and sometimes I was not too sure of what I was doing.

Programming assignments are a little difficult.

Programming assignments' quality is too bad to follow it.

Programming assignments need more instructions and less assumption on what the students already know.

A fine balance between theory and derivations, and practice with the programming assignments.

Programming assignments seemed to be written from a completely different direction, and instructions are vague and misleading.

Admittedly there are many gaps between the lectures and course materials and what is asked in programming assignments.

However, the programming assignments are very time consuming and not necessarily relevent good thing is it's trying to give you a sense of practically how to do it.downside is it's not really bridging to from maths to that practical sense in python (and the online jupyter notebook is terrible).the teaching staff is actually more responsive than the other 2 in the specialization.a bit more sided on python than maths though.

The programming assignments are more about numpy/python peculiarities (which dimension is D or N) and deciphering cryptographic specifications (X is documented as an input but not a parameter to the function).

Programming assignments is not difficult but hard to complete because of vaguely clarification.Plenty of time wasted to find what should i do, its' really frustrating.

The lecturer skips over things way too fast and delivers poor explanations, and then gives ridiculously hard programming assignments when this course is supposed to be mainly about maths.

Far more challenging than the first two courses.

This course does require you to have some prior experience, though, so if you are new to programming or linear algebra (not just the concepts but how to apply them) it's bets to take the first two courses with some additional help (maybe Khan academy or even MIT OCW.

Like the first two courses of the specialization, this course is shallow, shouldn't be anyone's introduction to the subject and is a refresher at best.

I've finished all the two previous courses in this specialization.I was shocked at seeing the content and programming assignments given to us.It was totally different.They expect a lot from us.Content is not up to the mark.First two courses was awesome.But this course is an exact opposite to the first two.Totally disappointed!!

Loved the first two courses but felt like killing myself in this course.

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

First two courses in this series are great but not this one.

The first two courses in this series are excellent.

The knowledge that you've gathered throughout the first two courses gets applied here.

Pretty bad all around.The teacher keeps throwing formulas without taking the time to explain why they are useful, and what they represent.The first two courses were really good, and this one is a bummer.Most of what I learned was learned elsewhere, the course acted as a detailed syllabus with some practice quiz (of relatively poor quality).

It's still worth taking if you completed the first two courses and want the specialization certification.

Compared to the first two courses in this specialisation, this course was not very engaging.

However, it just like the ingredients the math for machine learning will not be complete without attempting to this one.

its a good course to learn mathematics essential for machine learning This course demystifies the Principal Components Analysis through practical implementation.

Requires a fair amount of external reading/referencing for some of the concepts which seem to be covered only at a high level in the lectures.I would love to see more courses on applied mathematics for machine learning.

Very interesting and challenging subject: PSA, this MOOC together with the other 2 Mathematics for Machine Learning are one of the most useful I have ever made, actually they helped a lot in my other Machine learning and Deep learning studies!

The course is mathematics for Machine Learning.

I encourage anyone doing this course to read Deisenroth's free book Mathematics for Machine Learning (mml-book.com) to better understand the notation and technique used to get to the proofs.

It will be great to have a course on Statistics for Machine learning covering advanced concepts in probability theory.

A solid background in linear algebra is required in order to fully understand everything.

An excellently simple explanation of concepts of linear algebra and PCA.

Would give this course 5 stars if it was properly described so that expectation could match reality:Give yourself plenty of time for this course - it will take quite a bit longer than described.Make sure you are comfortable with Python and NumPy before you start (particularly the linear algebra functions).It is very different (much less hand-holding) than the other courses in the specialization.

Loved the way the lectures were delivered and the programming assignments help to build a strong base for applications of linear algebra that we have done earlier.Thanks and RegardsJitesh Tripathi, PhD in Applied Mathematics The course has two problems:complete lack of participation of staff in maintaining it.

This course is the worst of the module with linear algebra and multivariate calculus being much better Assignment 1 cannot be passed!

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

Other than that, the lectures are actually very good, but the only one worth the time is the fourth one, the other three are just the first course (Linear Algebra) all over again.

Learnt a lot, a lot of Linear Algebra, Projections/ Geometry/ all of these Mathematical ideas would help greatly in understanding of Machine Learning concepts and applying them to real world data!!..

I felt utterly frustrated at times.

At times this is frustrating but yet that's the best way to build your own intuition.

Only flaw is that programming assignments are poorly designed in some places and are quite difficult to pick up at times.

I think that the course would have benefited from a more aneddoctical approach at times: for instance restating in english what the general purpose of PCA is, could help the less mathematically inclined to better seize the idea.

At times it was tough to follow and could have been better if there are some additional examples explained to reinforce the concept.

One wastes so much time trying to figure out the solution.

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

The advice students give each other are frankly so wrong it is shocking.the teacher focuses on formalised proof rather than concepts.

Also, this isn't a course on unit testing - some more tests should be included to help students debug individual functions rather than relying on the final algorithm (e.g.

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

It's not enough exercises last week.

The programming assignment require a lot of my effort in programming, but not much on math.I'm not saying that this course is very bad, but Compare to the previous 2 course in the Math for ML specialization, provided by the same university, this one is obviously inferior.

This course is very helpful for me to understand Math for ML.