Bayesian Networks, Markov Chains, Hidden Markov Models
What you'll learn
Probability theorem
Conditional Independence
Bayesian Networks
Probabilistic Graphical Models
Markov Property
Requirements
Basic Understanding of Programming
Python Fundamentals
Probability Theorem
Description
"Real world often revolves around uncertainty. Humans have to consider a degree of uncertainty while taking decisions. The same principle applies to Artificial Intelligence too. Uncertainty in artificial intelligence refers to situations where the system lacks complete information or faces unpredictability in its environment. Dealing with uncertainty is a critical aspect of AI, as real-world scenarios are often complex, dynamic, and ambiguous. This course is a primer on designing programs and probabilistic graphical models for taking decisions under uncertainty. This course is all about Uncertainty, causes of uncertainty, representing and measuring Uncertainty and taking decisions in uncertain situations. Probability gives the measurement of uncertainty. We will go through a series of lectures in understanding the foundations of probability theorem. we will be visiting Bayes theorem, Bayesian networks that represent conditional independence. Bayesian Networks has found its place in some of the prominent areas like Aviation industry, Business Intelligence, Medical Diagnosis, public policy etc.In the second half of the course, we will look into the effects of time and uncertainty together on decision making. We will be working on Markov property and its applications. Representing uncertainty and developing computations models that solve uncertainty is a very important area in Artificial Intelligence"
Who this course is for
Anyone interested in the field of Artificial Intelligence
Homepage

https://www.udemy.com/course/foundations-of-ai-actions-under-uncertainty/