Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.
Covers t-tests, p-values calculation, and comparison of coefficients.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
