In a world of incomplete information, probability quantifies uncertainty. Master the math behind machine learning, risk analysis, and statistical inference.
Bayesian models, classification, and prediction.
Hypothesis testing and confidence intervals.
Risk modeling and portfolio optimization.
What: Sample Space, Axioms, Conditional Probability, Independence, Bayes' Theorem.
Why: Master the logic of uncertainty using sets and conditional rules.
What: Bernoulli, Binomial, Poisson, Uniform, Exponential, Normal.
Why: Model real-world phenomena with discrete and continuous variables.
What: Expectation, Mean, Median, Mode, Variance.
Why: Summarize data through theoretical and descriptive centers.
What: Advanced probability problems and GATE practice.