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Related lectures (31)
Unsupervised Learning: Clustering & Dimensionality Reduction
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Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
Clustering Methods
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Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
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By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
PCA: Key Concepts
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Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Unsupervised Learning: PCA & K-means
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Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Unsupervised Learning: Dimensionality Reduction and Clustering
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Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.
Reinforcement Learning Concepts
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Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Auto-encoder and GANs
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Covers auto-encoders for data compression and GANs for data generation.
K-means Clustering: Initialization and Image Segmentation
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Explores k-means clustering, emphasizing initialization and image segmentation.
PCA: Directions of Largest Variance
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Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Principal Component Analysis: Applications and Limitations
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Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.
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