About this Course
최근 조회 185,236

다음 전문 분야의 3개 강좌 중 2번째 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: 6 weeks of study, 2-5 hours/week...

영어

자막: 영어, 그리스어, 스페인어

귀하가 습득할 기술

Linear RegressionVector CalculusMultivariable CalculusGradient Descent
Course을(를) 수강하는 학습자
  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Data Analysts
  • Scientists

다음 전문 분야의 3개 강좌 중 2번째 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: 6 weeks of study, 2-5 hours/week...

영어

자막: 영어, 그리스어, 스페인어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

What is calculus?

10개 동영상 (총 46분), 4 readings, 6 quizzes
10개의 동영상
Welcome to Module 1!1m
Functions4m
Rise Over Run4m
Definition of a derivative10m
Differentiation examples & special cases7m
Product rule4m
Chain rule5m
Taming a beast5m
See you next module!39
4개의 읽기 자료
About Imperial College & the team5m
How to be successful in this course5m
Grading Policy5m
Additional Readings & Helpful References5m
6개 연습문제
Matching functions visually20m
Matching the graph of a function to the graph of its derivative20m
Let's differentiate some functions20m
Practicing the product rule20m
Practicing the chain rule20m
Unleashing the toolbox20m
2
완료하는 데 3시간 필요

Multivariate calculus

9개 동영상 (총 41분), 5 quizzes
9개의 동영상
Variables, constants & context7m
Differentiate with respect to anything4m
The Jacobian5m
Jacobian applied6m
The Sandpit4m
The Hessian5m
Reality is hard4m
See you next module!23
5개 연습문제
Practicing partial differentiation20m
Calculating the Jacobian20m
Bigger Jacobians!20m
Calculating Hessians20m
Assessment: Jacobians and Hessians20m
3
완료하는 데 3시간 필요

Multivariate chain rule and its applications

6개 동영상 (총 19분), 4 quizzes
6개의 동영상
Multivariate chain rule2m
More multivariate chain rule5m
Simple neural networks5m
More simple neural networks4m
See you next module!34
3개 연습문제
Multivariate chain rule exercise20m
Simple Artificial Neural Networks20m
Training Neural Networks25m
4
완료하는 데 2시간 필요

Taylor series and linearisation

9개 동영상 (총 41분), 5 quizzes
9개의 동영상
Building approximate functions3m
Power series3m
Power series derivation9m
Power series details6m
Examples5m
Linearisation5m
Multivariate Taylor6m
See you next module!28
5개 연습문제
Matching functions and approximations20m
Applying the Taylor series15m
Taylor series - Special cases10m
2D Taylor series15m
Taylor Series Assessment20m
4.7
288개의 리뷰Chevron Right

37%

이 강좌를 수료한 후 새로운 경력 시작하기

29%

이 강좌를 통해 확실한 경력상 이점 얻기

Mathematics for Machine Learning: Multivariate Calculus의 최상위 리뷰

대학: JTNov 13th 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.

대학: SSAug 4th 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.

강사

Avatar

Samuel J. Cooper

Lecturer
Dyson School of Design Engineering
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David Dye

Professor of Metallurgy
Department of Materials
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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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