Introduction to Artificial Intelligence

Program Objectives

To introduce the fundamental concepts and principles of Artificial Intelligence.
To develop an understanding of intelligent agents and problem-solving techniques.
To provide basic knowledge of machine learning and data-driven approaches.
To familiarize learners with AI applications across various industries.
To build analytical and logical thinking skills for designing AI-based solutions.
To create awareness about ethical, social, and legal aspects of AI.

Learning Outcomes

By the end of this course, learners will be able to:

Understand and explain core concepts and terminology of Artificial Intelligence.
Identify different types of AI systems and their real-world applications.
Apply basic search and problem-solving techniques in AI.
Demonstrate understanding of machine learning fundamentals and simple algorithms.
Analyze how AI technologies like NLP and Computer Vision work at a basic level.
Evaluate the impact of AI on society, including ethical considerations.
Develop simple AI-based models or solutions using basic tools and techniques.

Course Outline

Module 1:

Introduction to AI

Definition and scope of AI
History and evolution of AI
Types of AI (Narrow AI, General AI, Super AI)
Applications of AI in various industries
AI vs Machine Learning vs Deep Learning

Module 2:

Intelligent Agents

Concept of agents and environments
Types of agents (simple reflex, model-based, goal-based, utility-based)
Rationality and performance measures
Agent architecture

Module 3:

Problem Solving & Search Techniques

Concept of agents and environments
Types of agents (simple reflex, model-based, goal-based, utility-based)
Rationality and performance measures
Agent architecture

Module 4:

Knowledge Representation

Logic (Propositional & First-Order Logic)
Knowledge-based systems
Semantic networks
Frames and ontologies

Module 5:

Reasoning & Inference

Deductive and inductive reasoning
Forward and backward chaining
Resolution method
Handling uncertainty

Module 6:

Introduction to Machine Learning

What is Machine Learning
Types: Supervised, Unsupervised, Reinforcement Learning
Basic algorithms (Linear Regression, Decision Trees, KNN)
Model evaluation concepts

Module 7:

Neural Networks & Deep Learning (Basics)

Biological neuron vs artificial neuron
Perceptron model
Introduction to neural networks
Basics of deep learning

Module 8:

Natural Language Processing (NLP)

Introduction to NLP
Text preprocessing
Applications (chatbots, translation, sentiment analysis)

Module 9:

Computer Vision

Basics of image processing
Object detection
Applications (face recognition, self-driving cars)

Module 10:

Ethics & Future of AI

Ethical concerns in AI
Bias and fairness
Privacy and security
Future trends in AI

Module 11:

AI Tools & Applications

Introduction to popular AI tools (Python, TensorFlow, etc.)
Real-world case studies
Hands-on mini project

Assessment Methods

Assignments
Quizzes
Mini Project
Final Exam / Presentation

Portfolio Information

Client

Andrew Lim

Date

25 September 2026

Address

45 King Street, Covent Garden, London WC2E 8JW

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

Program Objectives

Learning Outcomes

Course Outline

Module 1:

Module 2:

Module 3:

Module 4:

Module 5:

Module 6:

Module 7:

Module 8:

Module 9:

Module 10:

Module 11:

Assessment Methods

Portfolio Information

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