Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Introduction to Mathematical F

Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Introduction to Mathematical F

Assessment

Interactive Video

Information Technology (IT), Architecture, Mathematics

University

Hard

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This video tutorial covers essential concepts in feature extraction, a key phase in dimensionality reduction. It explains the importance of vector spaces, subspaces, eigen decomposition, and positive semi-definite matrices. The tutorial also delves into singular value decomposition and constrained optimization using Lagrangian multipliers. The instructor aims to simplify these mathematical concepts for viewers without a strong mathematical background, emphasizing their relevance in data science.

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2 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe the relationship between singular value decomposition and principal component analysis.

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

How can vector and matrix derivatives be applied in optimizing Lagrangian functions?

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