Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
