Variational Inference and Neural Networks

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Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

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

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

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