Introduction to AI

Introduction to AI

This is an introductory course for Executives. It doesn't delve too much into the technicality or the mathematics of the subject, but gives the learner enough knowledge to be able to make decisions on the use of Artificial Intelligence and Machine Learning for his / her businesses.

If the learner is more inclined towards technical development this course will equip the learner with the tools and knowledge to leap forward towards AI and ML integration and decide on the best usage of technology suited to his / her business needs.

Learning Outcome

At the end of this course the learner will

• Have a clear idea on what Artificial intelligence is.
• Understand what Machine Learning is.
• Know about the various types and categories of AI and ML.
• Know how they work.
• Be able to decide on how it can benefit his / her businesses.
• Have the ability to decide on what technologies to use to achieve integration goals with AI and ML.
• Be able to predict the trend of this technology and impact on the global economy and technology.
• An overall understanding of the technologies at work behind the scenes and how to use them for the learner's advantage and goal.

Duration

One day

Prerequisites

Some ideas about Information and Communication Technology. Ability to use the internet. Basic understanding of how programming works.

Outline

1. Introduction
2. Foundations of AI.
3. AI practical implementations
   1. Robotics.
   2. Speech Recognition.
   3. Autonomous Planning & Scheduling.
   4. Game Playing.
   5. Spam Filtering.
   6. Logistic Planning.
   7. Machine Translation.
4. Intelligent Agents.
   1. Table-driven agents.
   2. Simple Reflex Agents.
   3. Model Based Reflex Agents.
   4. Goal Based Agents.
   5. Utility Based Agents.
   6. Learning Agents.
5. Logical Agents.
   1. Knowledge-Based Agents.
   2. Logic.
   3. Propositional Logic.
   4. Agent-based Propositional Logic.
6. First Order Logic.
7. Inferences in First Order Logic.
8. Knowledge Representation.
9. Quantifying Uncertainty.
10. Probabilistic Reasoning.
11. Learning
    1. Learning from Examples.
    2. Knowledge in Learning.
    3. Learning Probabilistic Models.
12. Machine Learning.
13. Deep Learning.
14. Natural Language Processing.
15. Perception.
    1. Image Formation.
    2. Object Recognition.
    3. Computer Vision.
16. Robotics.
    1. Robotic Perception.
    2. Robotic Software Architecture.
17. Semantic Web.
18. Present and Future.
19. Ethics, Risks of AI Development.