Basics of likelihood, sample spaces, and events.
Addition and multiplication rules, conditional probability, and Bayes' theorem.
Discrete and continuous random variables and their distributions.
Linear algebra foundations for understanding high-dimensional spaces.
Understanding change in multiple dimensions.
The optimization algorithm behind training neural networks.
Predicting continuous values using line fitting techniques.
Categorizing data into distinct classes using logistic regression and other classifiers.
Building complex models inspired by the human brain.