Lectures related to Gaussian Mixture Models & Noisy Signals

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

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores continuous random variables, density functions, and distribution laws with practical examples.

Covers decoding methods and training challenges in natural language generation.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.