Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Covers logistic regression for linear classification and unsupervised dimensionality reduction techniques.
