Foundations of A.I.: Actions Under Uncertainty

Focused View

Prag Robotics

3:07:04

7 View

1. About the Program

1. Course Introduction.mp4

02:59

2. Course Outline.mp4

01:35

2. Actions Under Uncertainty

1. Actions Under Uncertainty.mp4

08:04

2. Probability Notation.mp4

14:36

3. Independence and Conditional Independence.mp4

11:11

4. Action Under Uncertainty.html

3. Software Installation

1. Installing Anaconda Distribution.mp4

03:13

2. Handling Jupyter Notebooks 1.mp4

02:53

3. Handling Jupyter Notebooks 2.mp4

02:04

4. Handling Jupyter Notebooks 3.mp4

03:19

5. Handling Jupyter Notebooks 4.mp4

06:01

6. Handling Jupyter Notebooks 5.mp4

03:32

4. Bayesian Networks

1. Bayes Theorem.mp4

12:29

2. Bayesian Networks.mp4

10:40

3. Implementation of Bayesian Networks.mp4

22:55

4. Inference in Bayesian Networks.mp4

09:10

5. Applications of Bayesian Networks.mp4

06:02

6. Bayesian Networks.html

5. Time and Uncertainty

1. Time and Uncertainty.mp4

09:56

2. Markov Chains.mp4

12:52

3. Implementation of Markov Chain.mp4

10:52

4. Hidden Markov models.mp4

15:39

5. Implementation of HMM in Python.mp4

14:59

6. Time and Uncertainty.html

6. About the Program

1. Course Conclusion.mp4

02:03

Description

Bayesian Networks, Markov Chains, Hidden Markov Models

What You'll Learn?

Probability theorem
Conditional Independence
Bayesian Networks
Probabilistic Graphical Models
Markov Property

Who is this for?

Anyone interested in the field of Artificial Intelligence

What You Need to Know?

Basic Understanding of Programming

Python Fundamentals

Probability Theorem

More details

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

User Reviews

Rating

average 0

Total votes0

Focused display

Artificial Intelligence

Prag Robotics

Instructor's Courses

Prag Robotics is a “Centre of Excellence” for Robotics and Artificial Intelligence studies, has very quickly established a mark in manufacturing & academic circles. Prag has established RoboLab–4.0 in Universities, Colleges and Schools, delivering educational programs in the emerging technologies viz. Robotics, Artificial Intelligence and Data Science – deploying the “Real-time” Robots, Equipment and most recent Software. These intricately designed programs are preparing our students to compete with the best in the world of Robotics and Automation.The course curriculums are in line with 23 legendary universities across the globe and industry insights with our superlative industry connect. Our programs are clear, to-the-point, expertly designed and delivered by roboticists who have accomplished their masters from eminent educational institutions.In this journey, conducted awareness sessions for over 18000 students and individuals, educated 6000+ young engineers, made them industry ready thus supported them to find placement in their domain or pursue their higher education with ease.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.