(Mathematics for Machine Learning の専門分野)

May 28 より開講

(Mathematics for Machine Learning の専門分野)

Mathematics for Machine Learning。Learn about the prerequisite mathematics for applications in data science and machine learning

この専門講座について

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths - 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 specialisation aims to bridge that gap, getting you up to speed in the underlying maths, 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 optimise 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 Components Analysis, uses the maths from the first two courses to do simple optimisation for the situation where you don’t have an understanding of how the data variables relate to each other. At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

作成者:

courses
3 courses

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プロジェクト

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プロジェクトの概要

コース
Beginner Specialization.
No prior experience required.
  1. コース 1

    Mathematics for Machine Learning: Linear Algebra

    今後のセッション:May 28
    所要時間
    5 weeks of study, 2-5 hours/week
    字幕
    English

    コースについて

    In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, an
  2. コース 2

    Mathematics for Machine Learning: Multivariate Calculus

    今後のセッション:May 28
    所要時間
    6 weeks of study, 2-5 hours/week
    字幕
    English

    コースについて

    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 thi
  3. コース 3

    Mathematics for Machine Learning: PCA

    今後のセッション:May 28
    所要時間
    4 weeks of study, 3-5 hours/week
    字幕
    English

    コースについて

    This 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

作成者

  • インペリアル・カレッジ・ロンドン(Imperial College London)

    An Imperial education is something special. Learn from world class experts and be part of a global community, sharing ideas, expertise and technology to find answers to the big scientific questions and tackle global challenges.

    Imperial College London is a world top ten university with an international reputation for excellence in science, engineering, medicine and business. located in the heart of London. Imperial is a multidisciplinary space for education, research, translation and commercialisation, harnessing science and innovation to tackle global challenges. Imperial students benefit from a world-leading, inclusive educational experience, rooted in the College’s world-leading research. Our online courses are designed to promote interactivity, learning and the development of core skills, through the use of cutting-edge digital technology.

  • David Dye

    David Dye

    Professor of Metallurgy
  • Samuel J. Cooper

    Samuel J. Cooper

    Lecturer
  • Marc P. Deisenroth

    Marc P. Deisenroth

    Lecturer in Statistical Machine Learning
  • A. Freddie Page

    A. Freddie Page

    Strategic Teaching Fellow

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