Probability and Statistics

Probability and Statistics

Basic Probability Concepts

• Probability spaces, events, and outcomes

• Conditional probability and independence

• Random Variables and Distributions

• Discrete and continuous random variables

• Common probability distributions: Normal, Binomial, Poisson, etc.

• Expectation and Variance

• Mathematical Expectation and Algorithm Evaluation

• 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:

• Bayes' theorem and its application in machine learning

  • Application in Bayesian Classification

  • Probabilistic Graphic Models

  • Bayesian Learning Algorithms

  • Bayes Theorem in Parameter Estimation