Artificial Intelligence (A.I) Essentials

Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent machines that can perform tasks that normally require human intelligence, such as:

• Perception
• Reasoning
• Learning
• Decision making
• Natural language processing.

AI can be applied in a wide range of fields, including healthcare, finance, education, transportation, and entertainment.

It has become an essential part of the technology industry. Research associated with artificial intelligence is highly technical and specialised.

Objective

• History of Artificial Intelligence
• What is Artificial Intelligence?
• Application of Artificial Intelligence
• Artificial Intelligence – Intelligent Systems
• Problems in Artificial Intelligence
• Approaches used in Artificial Intelligence
• Tools used in Artificial Intelligence
• Different types of Artificial Intelligence Technology
• Agents and Environments in Artificial Intelligence
• Artificial Intelligence Expert Systems
• Artificial Intelligence – Robotics
• What is strong and weak Artificial Intelligence?

Course Outline

Day 1

Introduction to AI

• What is AI?
• Brief history of AI
• Different types of AI and their applications
• Key concepts and terminology

Day 2

Machine Learning

• Introduction to machine learning
• Supervised, unsupervised, and reinforcement learning.
• Classification and regression
• Overfitting and underfitting
• Model evaluation and selection

Day 3

Neural Networks and Deep Learning

• Introduction to neural networks
• Structure and working of a neural network.
• Activation functions
• Convolutional neural networks (CNN) and their applications
• Recurrent neural networks (RNN) and their applications

Day 4

Natural Language Processing

• Introduction to NLP
• Tokenization, stemming, and lemmatisation.
• Part-of-speech tagging
• Named entity recognition.
• Sentiment analysis

Day 5

Future of AI and Ethics

• Emerging trends in AI
• AI and its impact on society
• Ethical considerations in AI development and use
• Bias and fairness in AI
• Explainable AI