FM2AZPA20 Practical Artificial Intelligence
FM2AZPA20 Practical Artificial Intelligence
- Course description
- Course CodeFM2AZPA20
- Level of Study5.2
- Program of StudyApplied Artificial Intelligence
- Credits20
- Study Plan CoordinatorBertram Haskins
This course provides students with hands-on experience in developing and deploying artificial intelligence solutions across various domains. Candidates will explore various AI applications, including natural language processing, machine vision, generative AI models, and reinforcement learning.
The course emphasises the practical implementation of AI techniques using several tools and platforms, such as transformer architectures, large language models, and prompt engineering. Students will also engage with AI platforms and automated agent frameworks for goals ranging from text to image generation. The semester culminates in a project where students will apply their acquired knowledge to develop an AI-based solution, reinforcing both technical expertise and real-world problem-solving skills.
The candidate:
- has a thorough understanding of AI application domains and their real-world uses and practical implications.
- is familiar with natural language processing (NLP) techniques, including the use of transformer architectures and large language models for applied AI tasks.
- understands machine vision concepts and how they are applied in real-world scenarios.
- has knowledge of generative AI models, including their creation, training, deployment, and ethical considerations.
- has knowledge of the principles of automated agents and their design and deployment in AI-driven environments.
- understands the foundations and real-world applications of reinforcement learning.
- is familiar with AI platforms and tools, and their role in AI development and deployment.
The candidate...
- can apply knowledge to develop and deploy AI-driven applications in various fields, including NLP, machine vision, generative AI, and reinforcement learning.
- can apply knowledge to fine-tune and apply large language models, leveraging transformer-based architectures for AI tasks.
- can explain their choices when creating, deploying, and utilising generative AI models, including systems for text and image generation.
- can study a situation and identify optimisation possibilities for AI interactions using advanced prompt engineering techniques.
- can explain their choices when implementing reinforcement learning algorithms and testing them in various environments.
- can reflect on and justify their use of pre-trained AI models and platforms, integrating them into custom AI applications.
- can design and execute a semester project, demonstrating applied AI knowledge in a practical setting, meeting the needs of defined user or target groups.
The candidate:
- understands and applies ethical principles in AI practice, and has developed an ethical attitude toward responsible use of AI technologies.
- can plan and carry out AI projects independently or in collaboration with others, in accordance with ethical requirements and principles.
- can carry out work that responds to the needs of defined user or target groups, ensuring relevance and impact of AI solutions.
- can build professional relations and collaborate with peers across disciplines and with external stakeholders in AI projects.
- can exchange viewpoints and participate in discussions on AI practice, contributing to the development of good practice in the field.
- can develop and showcase AI methods, products, and services that are relevant to professional practice and innovation.
- can contribute to organisational development by applying and adapting AI technologies in professional contexts.
Digital Learning Resources
The learning management system (LMS) is the primary learning platform where students access most of their course materials. The content is presented in various formats, such as text, images, models, videos or podcasts. Each course follows a progression plan, designed to lead students through weekly modules at their own pace. Exercises and assignments (individual or in groups) are embedded throughout the courses to support continuous practice and assessment of the learning outcomes.
Campus Resources
In addition to the digital learning resources, campus students participate in physical learning activities led by teachers as part of the overall delivery.
Guidance
Guidance and feedback from teachers support students' learning journeys, and may be provided synchronously or asynchronously, individually or in groups, via text, video or in-person feedback.