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
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Prag Robotics
Robotics & A.I.
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