Mathematics for Machine Learning
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assignment Nível: Intermediário
chat_bubble_outline Idioma : Inglês
date_range Publicado em 20 de abril de 2021
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Informações principais

credit_card Valor 40€
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timer 64 total de horas

Sobre o conteúdo

For a lot of higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra, we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and NumPy knowledge.

At the end of this specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

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No prior experience required.


Programa de estudos

  • Course 1: Mathematics for Machine Learning: Linear Algebra
  • Course 2: Mathematics for Machine Learning: Multivariate Calculus
  • Course 3: Mathematics for Machine Learning: PCA


David Dye
Professor of Metallurgy, Department of Materials

A. Freddie Page
Strategic Teaching Fellow, Dyson School of Design Engineering

Samuel J. Cooper
Senior Lecturer, Dyson School of Design Engineering

Marc Peter Deisenroth
Lecturer in Statistical Machine Learning, Department of Computing


Criador do conteúdo

Imperial College London

Imperial College London (legally Imperial College of Science, Technology and Medicine) is a public research university located in London. In 1851, Prince Albert built his vision of an area for culture, including the Victoria and Albert Museum, Natural History Museum, Royal Colleges, Royal Albert Hall, and the Imperial Institute. In 1907, Imperial College was established by royal charter, merging the Royal College of Science, Royal School of Mines, and City and Guilds of London Institute. In 1988, the Imperial College School of Medicine was formed by combining with St Mary's Hospital Medical School. In 2004, Queen Elizabeth II opened the Imperial College Business School.

The main campus is located in South Kensington, with an innovation campus in White City. The college also has a research centre at Silwood Park, and teaching hospitals throughout London. The university focuses exclusively on science, engineering, medicine, and business. Imperial has an international community, with more than 59% of students from outside the UK and 140 countries represented on campus.

In 2019–20, Imperial is globally ranked 9th in the Times Higher Education World University Rankings, 9th in the QS World University Rankings, 24th in the Academic Ranking of World Universities, and 8th in Reuters The World's Most Innovative Universities. Student, staff, and researcher affiliations include 14 Nobel laureates, 3 Fields Medalists, 1 Turing Award winner, 74 Fellows of the Royal Society, 87 Fellows of the Royal Academy of Engineering, and 85 Fellows of the Academy of Medical Sciences.




A Coursera é uma empresa digital que oferece um curso on-line massivo e aberto, fundado pelos professores de computação Andrew Ng e Daphne Koller Stanford University, localizado em Mountain View, Califórnia.

O Coursera trabalha com as melhores universidades e organizações para disponibilizar alguns dos seus cursos on-line e oferece cursos em várias disciplinas, incluindo: física, engenharia, humanidades, medicina, biologia, ciências sociais, matemática, negócios, ciência da computação, marketing digital, ciência de dados. e outros assuntos.Cours

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