Data science

Related lectures (31)

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Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

On PCA includes interactive exercises and emphasizes minimizing information loss.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Explores linear dimensionality reduction through PCA, variance maximization, and real-world applications like medical data analysis.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Explores PCA for data simplification and Spike Sorting for shape identification.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.