Dimensionality Reduction
Master of Science in Computer Science · University · Machine Learning
This unit introduces dimensionality reduction techniques, including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Students will learn how to reduce the dimensionality of data while preserving its important features, leading to improved machine learning model performance.
Zum Üben gibt es 56 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Introduction to Dimensionality Reduction
- Principal Component Analysis (PCA)
- PCA Algorithm and Implementation
- Determining the Number of Principal Components
- Singular Value Decomposition (SVD)
- Relationship between PCA and SVD
- Applications of Dimensionality Reduction
- Benefits and Limitations of Dimensionality Reduction
Stichworte
- Dimensionality Reduction
- PCA
- SVD
- Data Compression
- Feature Extraction
- Eigenvalues
- Eigenvectors
- Singular Values
- Unsupervised Learning