學生對 伦敦帝国学院 提供的 Mathematics for Machine Learning: Linear Algebra 的評價和反饋

I very much enjoyed the content of this class. The professor for the first 4 weeks was great! The professor for the 5th week seemed to move at a slightly faster pace with less in-depth instruction. His visual aids were pretty groovy though.

Excellent course for anyone who wants to know the nuances of Linear Algebra and its applications.The applications are not just mentioned,but one gets hands-on experience applying the concepts they learned,in code.Hats Off to the entire team!

The course is really helpful to those seeking clarity on the concepts. Week 4 and 5 really will really demand your attention. Loved every single bit of this course.

Would be glad if course would have included more visualisation to play with.

This is a good linear algebra course intro. May not be the one for who is looking for mathematical rigorous but it's enough for machine learning. Linear Algebra is important but not all topics and this course highlights the needed materials.

I needed a quick refresh of my prior knowledge of linear algebra for my MSc course and I wasnt disappointed. I also appreciated the complementary python exercises and the effort to put the material into a context of a real world application.

I got a lot of intuition about some fundamental aspects of linear algebra. Rest of courses on maths was very rigorous in terms of methods. This was more inclined towards applications in machine learning. I enjoyed the entire learning process

It was fun to work through the course. Sometimes it was challenging as it has to be. Now I have a much better understanding of the topic. Especially appreciated is the approach of the instructors to build intuition: it worked for me, thanks!

I have never visioned mathematics the way it was taught here. I believe every Engineer may he/she be a an ML engineer or not must take this course to just fall in love of mathematics. This course will inherently motivate you to dig deeper.

I really enjoyed taking this course. I could review and learn for the first time some important topics for machine learning, in special the eigenvalues and eigenvectors classes. I'd like to thank the course's professors and collaborators.

An excellent course on the fundamentals of linear algebra. It was great revisiting all this topics. I would also say that some knowledge on linear algebra would be useful for taking this course given the fact that the lectures are quick.

Very good course. I liked it a lot. Some abstract thinking required. The last week is a bit less well explained but OK nonetheless.

In my experience, the estimated times for completing the work are a bit optimistic, it took me more time.

Excellent course. Best course for a beginner with fundamental math knowledge to start with linear algebra. But more advanced course might be needed if you really want to dig into the study of linear algebra as well as machine learning.

Great lecturers, excellent delivery of subject matter. This course did not disappoint me. It provides a concise yet in-depth revision of linear algebra as is relevant to machine learning. Looking forward to more from these instructors.

Offered the much needed perspective of linear algebra to develop actual understanding, than just solving problems without understanding why and how actual computation works. I would like to thank the professors for such a great course.

This course not only provided me the fundamental knowledge of Mathematics required to learn my next interested course of Machine Learning, but also helped me to kill the lockdown period due to covid-19 pandemic in a useful way at home.

Excellent course. Outstanding methodology. Great fun and intuition based leaning, kudos to David Dye, Sam Cooper and the ICL team. Thank you very much for bringing forward this course. Looking forward for many more courses from ICL :D

I thoroughly enjoyed this course. After using matrices and vectors for decades in my work, I have finally gained some intuition about what the dot-product operation, determinant and eigen-vectors actually represent. Thank you so much.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

This course has provided me a better understanding of linear algebra concepts specifically on how eigenvalues, eigenvectors, matrices, and vectors can actually be observed or used in engineering (or even in day to day) applications.

It was a very good learning and I enjoyed a lot. Hoping to take the advanced level courses in Machine learning and related areas. Thank you very much Professors David dye, Samuel J Cooper and A Freddie Page. Hoping to see you again.

Awesome course!!! The course is very helpful for those who are willing to build an intuition of linear algebra. The coding assignments are a bit easy for CS students, but allow you to understand what has been taught in the course.

An excellent introduction to the core linear algebra concepts needed to understand ML. I especially enjoyed the emphasis on how matrices transform vectors. It provided me a much stronger intuition of the geometry than I had before.

Excellent. Exactly what I needed. A linear algebra course in machine learning. Top notch presentations, materials, and explanations. A nice blend of concepts and detailed calculations especially in transformations and eigenvectors.

I loved this course! Both Prof David Rye and Prof Sam Cooper were amazing and used brilliant techniques to ensure creative learning. I enjoyed the eigen vectors and values and pagerank algo module a little too much! Thanks a lot!

The course contains very creative introductions to some of the linear algebra theories that I was already familiar with. Could get new intuitions and better, deeper understanding of those concepts. Really glad I took this course.