| Summary |
This module provides students with practical skills to integrate modern AI technologies into real-world software applications. Rather than focusing on mathematical foundations, the course emphasises hands-on experience with contemporary AI frameworks, APIs, and cloud services.
Students will explore the current AI landscape, including large language models, reinforcement learning systems, and generative AI tools. The curriculum covers prompt-engineering, model selection, performance evaluation, and the critical decision-making process of when and how to incorporate AI capabilities into software projects.
Through working with pre-trained models and various AI service providers, students will learn to implement AI-powered features and manage data pipelines for AI systems. Practical experience with popular frameworks and cloud-based services will enable students to build applications that incorporate diverse AI capabilities.
The module places strong emphasis on the ethical and practical considerations of AI deployment, including bias detection and mitigation, privacy concerns, explainability requirements, and the social implications of AI-powered systems. Students will explore the environmental impact of AI systems, examining energy footprints and how training-inference trade-offs are reshaping approaches to AI scaling and deployment. Students will develop critical thinking skills to distinguish between marketing hype and genuine AI potential, enabling them to make informed technical decisions in this rapidly evolving field and understand the real limitations versus the potential for impact of future AI solutions. |
