MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.12 GB | Duration: 3h 35m
Knowledge Representation Techniques, Machine 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.
