Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.
Covers the optimization of neuron models for coding and decoding in computational neuroscience.
Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
