Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a subfield of the computer. It enables computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that humans do it using their intelligence. In simple words, Artificial Intelligence means the power of a machine to copy human intelligent behavior. It is about designing machines that can think.
The target audience for this course includes students and professionals who are interested in learning robotics and biometrics. This course is also meant for people who are very keen on learning Artificial Intelligence.
After completing this course, you will be able to learn :
1 Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
2 Building intelligent agents (search, games, logic, constraint satisfaction problems)
3 Machine Learning algorithms
4 Applications of AI (Natural Language Processing, Robotics/Vision)
5 Solving real AI problems through programming with Python
There are no pre-requisites required for attending this Course.
This is a two days course syllabus and is a classroom-based instructor-led one.
Course Outline / Content :
The following Modules are included in this course :
1 Introduction to Artificial Intelligence
2 Artificial Intelligence Architecture
3 Tools and Platforms used in Artificial Intelligence
4 Data Preparation for Analysis – General Tasks and Tools
5 Algorithms used in AI
6 Model Fine Tuning Selection and Cross-validation
7 Case Study –Use cases
Course Objectives :
1 The objective of the course is to present an overview of artificial intelligence (AI) principles and approaches. Develop a basic understanding of the building blocks of AI as presented in terms of intelligent agents: Search, Knowledge representation, inference, logic, and learning.
2 This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
3 You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.