Uncertainty in AI with Bayes

Uncertainty plays a major role in various real-time applications of AI, such as medical diagnosis, automated car driving prediction, weather forecasting, etc. This course consists of the essential principles and techniques of uncertainty with artificial intelligence. Real-time uncertainty has significant obstacles such as noisy data, incomplete information, and the intrinsic randomness of real-world systems. This video lecture will describe the uncertainty in AI and probabilistic reasoning. Further, this course deals with probabilistic reasoning in AI techniques. Further, this course consists of probability theory techniques, which highlight the mathematical basics of reasoning in uncertain situations. The learners will understand Bayesian inference systems, Bayesian inference networks, conditional probability, joint probability, and Bayes theorem.

The proposed video lectures elaborate robust lecturing techniques that explain Bayesian networks in detail. After studying the course, the students will learn and build skills in uncertain situations and exact and approximate inference. in detail. This course produces precise inference methods with Gibbs sampling, variable inference, as well as Markov Chain Monte Carlo methods and belief propagation. These techniques balance accuracy and computational efficiency, rendering them vital for scalable AI applications. Furthermore, this course will provide the next stepping stone for understanding machine learning and deep learning concepts in detail.`