課程信息
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完成時間大約為18 小時

建議:4 weeks of study, 4-5 hours/week...

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字幕:英語(English)

您將獲得的技能

Python ProgrammingPrincipal Component Analysis (PCA)Projection MatrixMathematical Optimization
學習Course的學生是
  • Machine Learning Engineers
  • Data Scientists
  • Biostatisticians
  • Traders
  • Researchers

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為18 小時

建議:4 weeks of study, 4-5 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 5 小時

Statistics of Datasets

8 個視頻 (總計 27 分鐘), 6 個閱讀材料, 4 個測驗
8 個視頻
Welcome to module 141
Mean of a dataset4分鐘
Variance of one-dimensional datasets4分鐘
Variance of higher-dimensional datasets5分鐘
Effect on the mean4分鐘
Effect on the (co)variance3分鐘
See you next module!27
6 個閱讀材料
About Imperial College & the team5分鐘
How to be successful in this course5分鐘
Grading policy5分鐘
Additional readings & helpful references5分鐘
Set up Jupyter notebook environment offline10分鐘
Symmetric, positive definite matrices10分鐘
3 個練習
Mean of datasets15分鐘
Variance of 1D datasets15分鐘
Covariance matrix of a two-dimensional dataset15分鐘
2
完成時間為 4 小時

Inner Products

8 個視頻 (總計 36 分鐘), 1 個閱讀材料, 5 個測驗
8 個視頻
Dot product4分鐘
Inner product: definition5分鐘
Inner product: length of vectors7分鐘
Inner product: distances between vectors3分鐘
Inner product: angles and orthogonality5分鐘
Inner products of functions and random variables (optional)7分鐘
Heading for the next module!35
1 個閱讀材料
Basis vectors20分鐘
4 個練習
Dot product10分鐘
Properties of inner products20分鐘
General inner products: lengths and distances20分鐘
Angles between vectors using a non-standard inner product20分鐘
3
完成時間為 4 小時

Orthogonal Projections

6 個視頻 (總計 25 分鐘), 1 個閱讀材料, 3 個測驗
6 個視頻
Projection onto 1D subspaces7分鐘
Example: projection onto 1D subspaces3分鐘
Projections onto higher-dimensional subspaces8分鐘
Example: projection onto a 2D subspace3分鐘
This was module 3!32
1 個閱讀材料
Full derivation of the projection20分鐘
2 個練習
Projection onto a 1-dimensional subspace25分鐘
Project 3D data onto a 2D subspace40分鐘
4
完成時間為 5 小時

Principal Component Analysis

10 個視頻 (總計 52 分鐘), 5 個閱讀材料, 2 個測驗
10 個視頻
Problem setting and PCA objective7分鐘
Finding the coordinates of the projected data5分鐘
Reformulation of the objective10分鐘
Finding the basis vectors that span the principal subspace7分鐘
Steps of PCA4分鐘
PCA in high dimensions5分鐘
Other interpretations of PCA (optional)7分鐘
Summary of this module42
This was the course on PCA56
5 個閱讀材料
Vector spaces20分鐘
Orthogonal complements10分鐘
Multivariate chain rule10分鐘
Lagrange multipliers10分鐘
Did you like the course? Let us know!10分鐘
1 個練習
Chain rule practice20分鐘
4.0
218 個審閱Chevron Right

50%

完成這些課程後已開始新的職業生涯

48%

通過此課程獲得實實在在的工作福利

來自Mathematics for Machine Learning: PCA的熱門評論

創建者 JSJul 17th 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

創建者 JVMay 1st 2018

This course was definitely a bit more complex, not so much in assignments but in the core concepts handled, than the others in the specialisation. Overall, it was fun to do this course!

講師

Avatar

Marc Peter Deisenroth

Lecturer in Statistical Machine Learning
Department of Computing

關於 伦敦帝国学院

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....

關於 数学在机器学习领域的应用 專項課程

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....
数学在机器学习领域的应用

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