Foundations Of A.I. – Knowledge Representation & Learning

What you'll learn
To study the principles of Artificial Intelligence
To have deeper knowledge on various paradigms of Artificial Intelligence
To provide the knowledge about knowledge representation and reasoning
To understand the process of representing knowledge graphically
To have adequate knowledge in developing expert systems
Requirements
None
Description
In this course, we try to establish an understanding of how can computers or machines represent this knowledge and how can they perform inference. Representing information in the form of graphs, pictures and inferring information from pictures has been there since the inception of mankind. In this course, we look into few graphical methods of representing knowledge. In the second half of the course, we look into the learning paradigm. Learning or gaining information, processing information and reasoning are key concepts of Artificial Intelligence. In this course we look into the fundamentals of Machine Learning and methods that generalize knowledge. During this part of the journey, we will try to understand more about learning agent and how is it different from the other artificial intelligence agents. We will work on decision trees and simple linear regression as a part of machine learning in this course.Intelligence is a very complex element in Humans which drives our lives. Take a decision or hire a candidate or solve a problem, intelligence is the key contributor. Since the bronze age, we tried to understand the evolution of intelligence and what are the key aspects that promote intelligence. One key element in promoting intelligence is representing knowledge we have acquired and inferring from the existing knowledge or deduction.
Overview
Section 1: About the Program
Lecture 1 Course Introduction
Lecture 2 Course Outline
Section 2: What is Artificial Intelligence
Lecture 3 What is A.I.?
Lecture 4 A.I. Paradigms
Lecture 5 Applications of A.I.
Section 3: Software Installation
Lecture 6 Installing Anaconda Distribution
Lecture 7 Handling Jupyter Notebooks 1
Lecture 8 Handling Jupyter Notebooks 2
Section 4: Knowledge Representation
Lecture 9 Knowledge based Agents
Lecture 10 Representing knowledge
Lecture 11 Knowledge Representation Techniques
Lecture 12 Expert Systems
Lecture 13 Build Expert System in Python
Lecture 14 Semantic Networks
Lecture 15 Building a Semantic Network using Networkx
Section 5: Learning
Lecture 16 Introduction to Machine Learning
Lecture 17 Types of Machine Learning
Lecture 18 Decision Trees
Lecture 19 Decision Trees with Python
Lecture 20 Applications of Decision Trees
Lecture 21 Linear Regression
Lecture 22 Linear Regression in Python
Lecture 23 Applications of Linear Regression
Section 6: About the Program
Lecture 24 Course Conclusion
Anyone interested in the field of Artificial Intelligence
Homepage

https://www.udemy.com/course/foundations-of-ai-knowledge-representation-learning/

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