Foundations Of A.I.: Actions Under Uncertainty

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"

Overview

Section 1: About the Program

Lecture 1 Course Introduction

Lecture 2 Course Outline

Section 2: Actions Under Uncertainty

Lecture 3 Actions Under Uncertainty

Lecture 4 Probability Notation

Lecture 5 Independence and Conditional Independence

Section 3: Software Installation

Lecture 6 Installing Anaconda Distribution

Lecture 7 Handling Jupyter Notebooks 1

Lecture 8 Handling Jupyter Notebooks 2

Lecture 9 Handling Jupyter Notebooks 3

Lecture 10 Handling Jupyter Notebooks 4

Lecture 11 Handling Jupyter Notebooks 5

Section 4: Bayesian Networks

Lecture 12 Bayes Theorem

Lecture 13 Bayesian Networks

Lecture 14 Implementation of Bayesian Networks

Lecture 15 Inference in Bayesian Networks

Lecture 16 Applications of Bayesian Networks

Section 5: Time and Uncertainty

Lecture 17 Time and Uncertainty

Lecture 18 Markov Chains

Lecture 19 Implementation of Markov Chain

Lecture 20 Hidden Markov models

Lecture 21 Implementation of HMM in Python

Section 6: About the Program

Lecture 22 Course Conclusion

Anyone interested in the field of Artificial Intelligence