About this Course
최근 조회 420,949

다음 전문 분야의 3개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 22시간 필요

권장: 5 weeks of study, 2-5 hours/week...

영어

자막: 영어
User
Course을(를) 수강하는 학습자
  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Data Analysts
  • Software Engineers

귀하가 습득할 기술

Eigenvalues And EigenvectorsBasis (Linear Algebra)Transformation MatrixLinear Algebra
User
Course을(를) 수강하는 학습자
  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Data Analysts
  • Software Engineers

다음 전문 분야의 3개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

초급 단계

완료하는 데 약 22시간 필요

권장: 5 weeks of study, 2-5 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

Introduction to Linear Algebra and to Mathematics for Machine Learning

5개 동영상 (총 28분), 4 readings, 3 quizzes
5개의 동영상
Motivations for linear algebra3m
Getting a handle on vectors9m
Operations with vectors11m
Summary1m
4개의 읽기 자료
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references10m
3개 연습문제
Exploring parameter space20m
Solving some simultaneous equations15m
Doing some vector operations14m
2
완료하는 데 2시간 필요

Vectors are objects that move around space

8개 동영상 (총 44분), 4 quizzes
8개의 동영상
Modulus & inner product10m
Cosine & dot product5m
Projection6m
Changing basis11m
Basis, vector space, and linear independence4m
Applications of changing basis3m
Summary1m
4개 연습문제
Dot product of vectors15m
Changing basis15m
Linear dependency of a set of vectors15m
Vector operations assessment15m
3
완료하는 데 3시간 필요

Matrices in Linear Algebra: Objects that operate on Vectors

8개 동영상 (총 57분), 3 quizzes
8개의 동영상
How matrices transform space5m
Types of matrix transformation8m
Composition or combination of matrix transformations8m
Solving the apples and bananas problem: Gaussian elimination8m
Going from Gaussian elimination to finding the inverse matrix8m
Determinants and inverses10m
Summary59
2개 연습문제
Using matrices to make transformations12m
Solving linear equations using the inverse matrix16m
4
완료하는 데 6시간 필요

Matrices make linear mappings

6개 동영상 (총 53분), 4 quizzes
6개의 동영상
Matrices changing basis11m
Doing a transformation in a changed basis4m
Orthogonal matrices6m
The Gram–Schmidt process6m
Example: Reflecting in a plane14m
2개 연습문제
Non-square matrix multiplication20m
Example: Using non-square matrices to do a projection12m
4.7
678개의 리뷰Chevron Right

34%

이 강좌를 수료한 후 새로운 경력 시작하기

33%

이 강좌를 통해 확실한 경력상 이점 얻기

Mathematics for Machine Learning: Linear Algebra의 최상위 리뷰

대학: ECSep 10th 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

대학: PLAug 26th 2018

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

강사

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David Dye

Professor of Metallurgy
Department of Materials
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Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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A. Freddie Page

Strategic Teaching Fellow
Dyson School of Design Engineering

임페리얼 칼리지 런던 정보

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....

머신 러닝 수학 전문 분야 정보

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 basic 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....
머신 러닝 수학

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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