Foundations of Computational Probability and Inference

Uncertainty is a fundamental aspect of real-world data and decision-making. This course equips you with the tools to quantify and reason about that uncertainty effectively. By the end of this course, you will be able to apply foundational probabilistic concepts and statistical inference techniques to build computational models that analyze data, make informed predictions, and tackle problems involving inherent randomness. What you'll learn: * Understand the fundamental axioms and rules of probability theory. * Apply concepts of random variables, probability distributions, and expected values. * Learn foundational methods of statistical inference, including estimation and hypothesis testing. * Implement computational techniques like Monte Carlo simulations for probabilistic modeling. * Grasp the principles of Bayesian inference for updating models with new data. * Explore basic concepts of information theory for quantifying uncertainty in data. * Develop an understanding of how probabilistic models underpin machine learning algorithms. The course begins with essential probability theory, then progresses to statistical inference and computational techniques. It concludes by demonstrating how these principles are applied in practical data analysis and predictive modeling scenarios. This course is designed for absolute beginners with no prior experience in probability, statistics, or computational modeling. No prerequisites are required. Begin your journey into the fascinating world of computational probability and inference today.