Probability and Statistics
Basic Probability Concepts
Fundamentals of Probability
Discrete and Continuous Distributions
Joint and Marginal Probabilities
Probability spaces, events, and outcomes
Conditional probability and independence
Random Variables and Distributions, Expectations
Discrete and continuous random variables
Common probability distributions: Normal, Binomial, Poisson, etc.
Expectation and Variance
Mathematical Expectation and Algorithm Evaluation
Statistical Measures: Mean, Variance, and Standard Deviation
Covariance and correlation
Statistical Inference
Hypothesis testing and confidence intervals
Maximum likelihood estimation (MLE)\
Descriptive and inferential statistics applied to AI
Measures of Central Tendency and Dispersion
Data Visualization in AI
Statistical Inference for Supervised Learning
Hypothesis Testing in Validation of AI Models:
Conditional Probability and Bayes’ Theorem and its application in machine learning
Application in Bayesian Classification
Probabilistic Graphic Models
Bayesian Learning Algorithms
Bayes Theorem in Parameter Estimation
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