Contents
Probability and Statistics for Artificial Intelligence
Basic Concepts of Probability Theory
Discrete and Continuous Random Variables
Probability Distributions and Density Functions
Conditional Probability and Independence
Bayes' Theorem and Bayesian Inference
Expectation, Variance, and Moments
Joint and Marginal Distributions
Covariance and Correlation
Limit Theorems (Law of Large Numbers and Central Limit Theorem)
Statistical Inference and Estimation
Hypothesis Testing and Confidence Intervals
Regression Analysis and Correlation Models
Analysis of Variance (ANOVA)
Dimensionality Reduction and Principal Component Analysis (PCA)
Time Series Analysis and Forecasting
Random Processes and Stationarity
Markov Chains and Markov Decision Processes
Hidden Markov Models (HMMs)
Monte Carlo Methods and Sampling Techniques
Statistical Learning and Model Evaluation
Statistics Codes https://github.com/jiwook021/Algorithms/tree/master/Statistics
Podcasts
