IT1100.10 – Artificial Intelligence Foundations

This subject introduces the core concepts and practical applications of Artificial Intelligence, with a focus on machine learning and deep learning techniques. Students explore both the user-facing and technical aspects of AI, building a foundation for advanced study and industry practice. Topics include supervised and unsupervised learning, regression, decision trees, support vector machines, probabilistic classifiers, clustering, data preprocessing, feature selection, and dimensionality reduction. The subject also introduces neural networks, convolutional neural networks, and basic natural language processing for image and text analysis, while emphasising ethical and responsible AI development. Through Python-based practical exercises, students learn to design, implement, evaluate, and compare AI models for real-world problems, with Python fundamentals provided for those new to programming.