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Related lectures (31)

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Covers protein mass spectrometry, gel electrophoresis, liquid chromatography, and tandem mass spectrometry for protein identification.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Delves into Ruelle resonances for linear pseudo-Anosov maps, highlighting their importance in dynamical systems theory.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Covers stochastic models for communications, including stationarity, ergodicity, power spectral density, and Wiener filter.

Covers continuous-time Markov chains and Kolmogorov equations.

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.