Introduction to Artificial Intelligence: Course Syllabus 

Course Level: Beginner – No prior programming required Typical Duration: 12 Sessions

Course Overview

This foundational programme provides a comprehensive exploration of Artificial Intelligence (AI), synthesising theoretical principles with practical application. Students will examine the definition, mechanics, and everyday utility of AI systems while developing the critical thinking skills necessary to navigate ethical complexities and societal impacts. The curriculum balances technical understanding with hands-on modelling to ensure a holistic grasp of how AI learns from data to generate predictions and decisions.

Learning Objectives

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

Define Artificial Intelligence and distinguish it from Machine Learning and Deep Learning.

Explicate fundamental machine learning concepts and underlying algorithms.

Recognise and evaluate AI applications within daily life and various industry sectors.

Identify ethical concerns, privacy risks, and inherent biases within AI systems.

Construct a functional AI model to resolve a real-world problem using industry-standard tools.

Critically evaluate the broader societal impact and future trajectories of AI technologies.

Module Breakdown

Module 1: Introduction to AI (Definition, Perception, Learning, Decision-making, Problem-solving, Natural Language Understanding, Historical Context: 1956 Dartmouth Project, AI Winter, Expert Systems, AI vs ML vs DL).

Module 2: AI in Everyday Life (Navigation and Maps, Streaming Services, Voice Assistants, Social Media, Healthcare Diagnostics, Financial Fraud Detection, E-Commerce, Privacy, Bias, Transparency, and Job Displacement).

Module 3: How AI Thinks (The AI Pipeline: Data Collection, Pattern Recognition, Training Phase, Predictions, Structured and Unstructured Data, Data Quality, Classification, and Regression).

Module 4: Types of Learning (Supervised Learning, Unsupervised Learning, Reinforcement Learning, Linear Regression, K-Means Clustering, and Q-Learning).

Module 5: Machine Learning Basics (Traditional vs ML Programming, Spam Detection, Image Recognition, Smart Recommendations, and the ML Workflow: Problem Definition to Deployment).

Module 6: Understanding Data and Bias (Structured and Unstructured Data, Categorical and Numerical Data, Data Quality, Training Data Bias, Algorithmic Bias, Measurement Bias, and Fairness Frameworks).

Module 7: Neural Networks Explained (Biological Inspiration, Neurons/Nodes, Architecture: Input, Hidden, and Output Layers, Forward Pass, Backpropagation, Activation Functions: ReLU, Sigmoid, Tanh, and Softmax).

Module 8: AI in Language (Natural Language Processing, Tokenisation, Named Entity Recognition, Sentiment Analysis, Machine Translation, Word Embeddings, and Prompt Engineering Techniques).

Module 9: Ethics in AI (Privacy Risks, Differential Privacy, Job Displacement Strategies, Transparency/Explainability, Autonomous Accountability, and Utilitarian vs Deontological Frameworks).

Module 10: Constructing Your First AI Project (Project Workflow, Foundational Project Archetypes: Iris Classification, Sentiment Analysis, House Price Prediction, Essential Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, TensorFlow/Keras).

Module 11: Course Wrap-Up & Next Steps (Comprehensive Review, Final Project Finalisation, Advanced ML Pathways, MLOps, and Career Development Strategies).

 

Final Project & Resources

Final Project Guidelines Students are required to resolve a real-world problem by constructing a working AI model. The project must demonstrate a complete workflow, including problem definition, data preparation, algorithm selection, and performance evaluation. Final assessment is based on code quality, the depth of documentation (README), and the effectiveness of the final presentation or demonstration.

Support Services

Office Hours: Available for conceptual clarification, project guidance, and career advice.

Academic Support: Assistance provided by the Writing Centre for documentation help and study skills.

IT Support: Technical assistance for software environments, coding platforms, and accessibility services.