Voltar para Mathematics for Machine Learning: Multivariate Calculus

4.7

estrelas

4,254 classificações

•

761 avaliações

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future....

JT

12 de Nov de 2018

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

DP

25 de Nov de 2018

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.

Filtrar por:

por Nushaine F

•18 de Jul de 2019

This is my first time learning calculus (I'm a 16 y/o high-school sophomore), and I'm satisfied with this course. The instructors were great, and the assignments are awesome.

If I would suggest one improvement, it would be to give more examples in the lectures. Some lectures were packed with examples, and some had none at all. I had to often refer to Khan Academy and YouTube to learn the concepts which the instructors did not provide an example for. (Especially in Week 4). Sometimes this would frustrate me because it would take me hours to grasp a concept.

Having said this, this course is for you if: (1) - you want a refresher on fundamental calculus concepts that relate to machine learning, or (2) - if you want to learn calculus for the first time, and you have a strong desire to learn these concepts. But no matter what, DON'T GIVE UP and don't stop until you've completed the course.

I hoped this has helped and good luck on your ML journey!

por Tanuj J

•18 de Jan de 2019

Topics need to be covered more in depth. Too much information packed into this course. Instructor's explanations are also not clear most of the time. It will be hard to follow this course if you don't have some background with calculus.

por FKIE F J C

•24 de Out de 2019

I don't want to be too hard on this course since I really liked some parts of it. Especially, the instructor in Week 1 - 4 did a good job explaining the concepts and overall one can clearly see that a lot of effort was put into the creation of this course. However, I found that a lot of topics could be handled a lot more in-depth.

The assessment at the end of a week was not really challenging and does not require a deep understanding of the concepts. Some of the quizzes were more challenging but in the assessments it was often only required to answer questions based on graphs or other images of functions. Most of the programming assignments only required the student to fill in some easier blanks.

I still do not know what the Taylor Series Chapter was about. I guess this is an important concept but I was not sure how this relates to machine learning. If you call a course Math for Machine Learning, I would expect that you relate the concepts to Machine Learning.

Maybe, it is just me but I would have been glad if this course had offered more depth and took at least double the amount of time to complete. This would have been more rewarding, as I do not feel that I learned as much as I hoped for when I started this course.

por Marc P

•28 de Abr de 2019

The course is led by two instructor and my ratings is an average of the two performances. The videos in week 1 to 4 are absolutely outstanding and a pleasure to follow. The ones in week 5 and 6 are ok but not great. The use of quizzes and coding assignments throughout the course is very engaging and of great use for retention and application of the learned subjects.

por Valeria B

•17 de Jun de 2019

The first part of the course is fine. Towards the end, lots of interesting concepts explained too quickly. I'd rather have more detailed explanations, especially about linear and non-linear regression.

The examples are quite good.

por James L T

•12 de Nov de 2018

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

por Samresh

•4 de Ago de 2019

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.

por Daniel P

•26 de Nov de 2018

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.

por Oleg B

•11 de Dez de 2018

Excellent summaries of important points.

por Yan

•30 de Mar de 2019

Some errors confused many students. And they are remained unfixed.

por Seongwoo K

•24 de Set de 2019

This specialization consists of the courses which deliver essential mathematical background to ML learners. I think learners would feel confident and solid when diving into ML after taking this course.

Video lectures are great with clear graphics and lecturers are passionate with energy. The most outstanding part is the programming assignments: They are designed so elegantly that you can get intuition right away once you go through them. They are simply amazing.

Meanwhile, be aware that learning curves are often steep at some points. Without some basic python skill and ML knowledge, I guess quite many people would feel frustrated. But please don't give up and push it through to completion. You will be absolutely rewarded at the end.

Thank you for great contents, David and Sam.

por Eric P

•9 de Abr de 2019

Challenging in places but another great speedy introduction to the relevant maths and how they are applied to ML. The best thing about this course is that you learn the general mathematical concepts and then see them in action in ML through examples and exercises. It's great. I used this course to refresh my maths skills learned long ago. I also found the pace good: neither too slow or too fast. The course would probably be quite challenging for someone who never had exposure to the concept of matrix algebra or derivatives.

por Artem D

•10 de Ago de 2018

I really liked the teachers and everything they prepared for the students.

Lectures are entertaining, not boring.

Assignments are interesting. Especially, i've found very useful the structure of learning: (1) you have a short lecture, (2) you have a small quiz which continue to intriduce you to the topic and in parallel let you to try it on practice - it was really great!

Thank you a lot! I loved this course (as a previous one) so much!

por Matthias S

•13 de Mai de 2019

The first four weeks are excellently prepared and the programming assignments are almost too easy at some points. The last two weeks and a part on backpropagation in the first four weeks give a nice intro on how to apply the learned methods. In the last two weeks there were some minor flaws in some slides and it is less easy to follow but it is still very well presented.

por Ilja S S L L

•20 de Nov de 2019

Really like the approach that a learner should get the intuition and understand how things work graphically. Then a learner should understand how numerical methods work and how math concepts can be used in Python code to do some optimization. Also, the sandpit exercises are great to easily understand how gradient descent works, which is a very important concept in ML.

por Nelson S S

•22 de Dez de 2019

Excelente.

Muchas gracias por compartir generosamente su conocimiento.

Ha sido muy grato para mí repasar temas de cálculo multivariado, álgebra lineal y optimización.

Gracias COURSERA, Gracias MINTIC y Gracias a The Imperial College of London. Un abrazo a cada profesor que ha dado lo mejor de su enseñanza en cada uno de los videos que he observado.

por laszlo

•30 de Abr de 2018

Really helpful and informative course. Different from the traditional math course, this course focuses on the intuitive understanding of math rather than the calculation. The calculation part are done by python code, which lays a foundation for further machine learning course and shows how the mathematical concepts are used in practice.

por Yiran X

•6 de Jan de 2019

This is a great course! I have learnt a lot in this course. I have leant single variable calculus and linear algebra (freshman year difficulty), and this course is challenging but doable for me! All the assignments are designed carefully and interesting to complete! I would like to say thank you to all the instructors of this course:D

por Lee F

•18 de Set de 2018

This course was perfect for me. I took calculus years ago in high school and college, but had forgotten most of it. This course got me back to Jacobians and Hessians quickly . These are essential tools for optimizing multivariate functions and fitting data sets with lots of features to models. Really enjoyed the course!

por Andrii S

•20 de Jan de 2019

Excellent.

por Mahwish A

•26 de Abr de 2020

Second professor David is waste of time while the first one is excellent.

por Carsten H

•31 de Mar de 2018

Too many derivatives of pointless functions.

por Rina F

•14 de Nov de 2020

This is a very useful course for brushing up on college multivariate calculus as well as learning many new skills and concepts necessary for understanding machine learning. I especially enjoyed the fact that the lecturers focus on understanding concepts rather than rote formulas (although there is certainly a fair amount of hand solving equations to make sure you understand the mechanics and concepts). I gained an understanding of the Taylor Series and its significance that was lost to me in college courses (years ago). The lectures, visual aids, and especially the interactive graphics were very well done. Thanks to Imperial College for offering this course and to the lecturers for all their care in producing it.

por ash g

•18 de Mar de 2019

I am enjoying this course massively. I am on week 5 and the lecturer has been great so far. Some of the programming assignments are a bit easy as in some cases the blanks to fill in are rather self-explanatory.

The exercise questions progress in difficulty nicely and are sized well. References to tackle more questions to solidify the understanding could be good, however I recognise that the aim is to teach the intuition and then move on and apply it in Machine Learning examples, rather than being a mathematics course alone.

por Nelson F A

•22 de Mar de 2019

Very intense course. However, now that I have moved on to Andrew Ng's ML course, I am so glad I finished it. Understanding the math behind ML makes learning it so much more enjoyable. Before it was like shooting in the dark. My python code wouldn't and ML-concepts would take a lot of time and effort to sink in. Sometimes not at all... This course armed me with the tools to succeed in a career in ML and AI. Looking forward to finishing the specialization!

- Como encontrar propósito e sentido na vida
- Compreendendo a pesquisa médica
- Japonês para iniciantes
- Introdução à computação em nuvem
- Fundamentos de Mindfulness
- Fundamentos de Finanças
- Aprendizagem Automática
- Aprendizagem automática usando o Sas Viya
- A ciência do bem-estar
- Rastreamento de Contato com a Covid-19
- IA para todos
- Mercados Financeiros
- Introdução à Psicologia
- Introdução à AWS
- Marketing internacional
- C++
- Análise Preditiva e Mineração de Dados
- Aprendendo a Aprender da UCSD
- Programação para todos da Universidade do Michigan
- Linguagem R da JHU
- Treinamento de CPI do Google CBRS

- Processamento da Linguagem Natural (PLN)
- IA para Medicina
- Bom com palavras: escrita e edição
- Modelagem de doenças infecciosas
- A pronúncia do inglês americano
- Automatização de teste de software
- Aprendizagem profunda
- Python para todosPython para todos
- Ciência de Dados
- Fundamentos de negóciosFundamentos dos Negócios
- Habilidades em Excel para negócios
- Ciência de Dados com Python
- Finanças para todos
- Habilidades de comunicação para engenheiros
- Treinamento de vendas
- Desenvolvimento e gestão de marca pessoal
- Análise de Dados de Negócios da Wharton
- Psicologia Positiva da Universidade da Pensilvânia
- Aprendizagem Automática da Universidade de Washington
- Design Gráfico da CalArts

- Certificados profissionais
- Certificados MasterTrack
- Suporte de TI do Google
- Ciência de dados da IBM
- Engenharia de Dados do Google Cloud
- IA aplicada da IBM
- Arquitetura do Google Cloud
- Analista de Cibersegurança da IBM
- Automação da TI do Google com Python
- Profissional de Mainframe do IBM z/OS
- Gestão aplicada de projetos da UCI
- Certificado em Design Instrucional
- Certificado em Engenharia e Gerenciamento de Construção
- Certificado de Big Data
- Certificado de Aprendizagem Automática em Análise de Dados
- Certificado em Gestão de Inovação e Empreendedorismo
- Certificado de Sustentabilidade e Desenvolvimento
- Certificado de Serviço Social
- Certificado de IA e Aprendizagem Automática
- Certificado de Análise e Visualização de Dados Espaciais

- Graduações em Ciência da Computação
- Graduações em Negócios
- Graduações em Saúde Pública
- Graduações em Ciência de Dados
- Bacharelados
- Bacharelado em Ciência da Computação
- Mestrado em Engenharia Elétrica
- Conclusão de bacharelado
- Mestrado em Gestão
- Mestrado em Ciência da Computação
- Mestrado em Saúde Pública
- Mestrado em Contabilidade
- Mestrado em Tecnologia da Computação e da Informação
- MBA On-line
- Mestrado em Ciência de Dados Aplicada
- MBA Global
- Mestrado em Inovação e Empreendedorismo
- Mestrado em Ciência de Dados
- Mestrado em Ciência da Computação
- Mestrado em saúde pública