Data Representations & Processing

Related lectures (30)

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Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Explores sampling, inferential statistics, and effective experimentation in statistics.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores research process, variable types, causality vs correlation, and sampling strategies.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.