Foundations of A.I.- Actions Under Uncertainty

Foundations of A.I.: Actions Under Uncertainty, available at $19.99, with 22 lectures, 3 quizzes, and has 8 subscribers.

You will learn about Probability theorem Conditional Independence Bayesian Networks Probabilistic Graphical Models Markov Property This course is ideal for individuals who are Anyone interested in the field of Artificial Intelligence It is particularly useful for Anyone interested in the field of Artificial Intelligence.

Enroll now: Foundations of A.I.: Actions Under Uncertainty

Summary

Title: Foundations of A.I.: Actions Under Uncertainty

Price: $19.99

Number of Lectures: 22

Number of Quizzes: 3

Number of Published Lectures: 22

Number of Published Quizzes: 3

Number of Curriculum Items: 25

Number of Published Curriculum Objects: 25

Original Price: ?2,299

Quality Status: approved

Status: Live

What You Will Learn

Probability theorem

Conditional Independence

Bayesian Networks

Probabilistic Graphical Models

Markov Property

Who Should Attend

Anyone interested in the field of Artificial Intelligence

Target Audiences

Anyone interested in the field of Artificial Intelligence

“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”

Course Curriculum

Chapter 1: About the Program

Lecture 1: Course Introduction

Lecture 2: Course Outline

Chapter 2: Actions Under Uncertainty

Lecture 1: Actions Under Uncertainty

Lecture 2: Probability Notation

Lecture 3: Independence and Conditional Independence

Chapter 3: Software Installation

Lecture 1: Installing Anaconda Distribution

Lecture 2: Handling Jupyter Notebooks 1

Lecture 3: Handling Jupyter Notebooks 2

Lecture 4: Handling Jupyter Notebooks 3

Lecture 5: Handling Jupyter Notebooks 4

Lecture 6: Handling Jupyter Notebooks 5

Chapter 4: Bayesian Networks

Lecture 1: Bayes Theorem

Lecture 2: Bayesian Networks

Lecture 3: Implementation of Bayesian Networks

Lecture 4: Inference in Bayesian Networks

Lecture 5: Applications of Bayesian Networks

Chapter 5: Time and Uncertainty

Lecture 1: Time and Uncertainty

Lecture 2: Markov Chains

Lecture 3: Implementation of Markov Chain

Lecture 4: Hidden Markov models

Lecture 5: Implementation of HMM in Python

Chapter 6: About the Program

Lecture 1: Course Conclusion

Instructors

Prag Robotics
Robotics & A.I.

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