このコースについて

243,060 最近の表示

受講生の就業成果

50%

コース終了後に新しいキャリアをスタートした

48%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約19時間で修了
英語
字幕:英語

学習内容

  • Implement mathematical concepts using real-world data

  • Derive PCA from a projection perspective

  • Understand how orthogonal projections work

  • Master PCA

習得するスキル

Dimensionality ReductionPython ProgrammingLinear Algebra

受講生の就業成果

50%

コース終了後に新しいキャリアをスタートした

48%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約19時間で修了
英語
字幕:英語

提供:

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

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

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up80%(3,952 件の評価)Info
1

1

5時間で修了

Statistics of Datasets

5時間で修了
8件のビデオ (合計27分), 6 readings, 4 quizzes
8件のビデオ
Welcome to module 141
Mean of a dataset4 分
Variance of one-dimensional datasets4 分
Variance of higher-dimensional datasets5 分
Effect on the mean4 分
Effect on the (co)variance3 分
See you next module!27
6件の学習用教材
About Imperial College & the team5 分
How to be successful in this course5 分
Grading policy5 分
Additional readings & helpful references5 分
Set up Jupyter notebook environment offline10 分
Symmetric, positive definite matrices10 分
3の練習問題
Mean of datasets15 分
Variance of 1D datasets15 分
Covariance matrix of a two-dimensional dataset15 分
2

2

4時間で修了

Inner Products

4時間で修了
8件のビデオ (合計36分), 1 reading, 5 quizzes
8件のビデオ
Dot product4 分
Inner product: definition5 分
Inner product: length of vectors7 分
Inner product: distances between vectors3 分
Inner product: angles and orthogonality5 分
Inner products of functions and random variables (optional)7 分
Heading for the next module!35
1件の学習用教材
Basis vectors20 分
4の練習問題
Dot product10 分
Properties of inner products20 分
General inner products: lengths and distances20 分
Angles between vectors using a non-standard inner product20 分
3

3

4時間で修了

Orthogonal Projections

4時間で修了
6件のビデオ (合計25分), 1 reading, 3 quizzes
6件のビデオ
Projection onto 1D subspaces7 分
Example: projection onto 1D subspaces3 分
Projections onto higher-dimensional subspaces8 分
Example: projection onto a 2D subspace3 分
This was module 3!32
1件の学習用教材
Full derivation of the projection20 分
2の練習問題
Projection onto a 1-dimensional subspace25 分
Project 3D data onto a 2D subspace40 分
4

4

5時間で修了

Principal Component Analysis

5時間で修了
10件のビデオ (合計52分), 5 readings, 2 quizzes
10件のビデオ
Problem setting and PCA objective7 分
Finding the coordinates of the projected data5 分
Reformulation of the objective10 分
Finding the basis vectors that span the principal subspace7 分
Steps of PCA4 分
PCA in high dimensions5 分
Other interpretations of PCA (optional)7 分
Summary of this module42
This was the course on PCA56
5件の学習用教材
Vector spaces20 分
Orthogonal complements10 分
Multivariate chain rule10 分
Lagrange multipliers10 分
Did you like the course? Let us know!10 分
1の練習問題
Chain rule practice20 分

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機械学習のための数学専門講座について

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in 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....
機械学習のための数学

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    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • You will need good python knowledge to get through the course.

  • This course is significantly harder and different in style: it uses more abstract concepts and requires much more programming experience than the other two courses. Therefore, when you complete the full specialization, you will be equipped with a much more diverse set of skills.

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