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Related lectures (30)

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Signals & Systems I: Sampling and Reconstruction

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

Introduction to Sampling

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

Data Representations & Processing

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Markov Chain Monte Carlo: Sampling and Convergence

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Sampling: DT-time processing of CT signals

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

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

Metrics for Classification

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Sampling: Continuous Spatial Phenomena

Covers different spatial sampling procedures and properties in Geographic Information Systems.