Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores document classification methods, including k-Nearest-Neighbors, Naïve Bayes Classifier, transformer models, and multi-head attention.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
