FM2AZDA10 Development for Artificial Intelligence

FM2AZDA10 Development for Artificial Intelligence

  • Course description
    • Course Code
      FM2AZDA10
    • Level of Study
      5.2
    • Program of Study
      Applied Artificial Intelligence
    • Credits
      10
    • Study Plan Coordinator
      Bertram Haskins
Teaching Term(s)
2026 Autumn
2027 Spring
About the Course

This course equips students with the essential skills and knowledge required to develop and optimise AI-driven solutions effectively. Candidates will gain a comprehensive understanding of the machine learning workflow, including hardware considerations, data handling, and deep learning techniques. The course provides hands-on experience with industry-standard programming tools and libraries.

Students will explore advanced AI development concepts, including recommender systems, network and graph theory applications, model optimization, and explainable AI. A strong emphasis is placed on practical implementation, enabling students to build a portfolio showcasing their expertise.

Course Learning Outcomes
Knowledge

The candidate...

  • has a thorough understanding of the machine learning workflow and its role in AI development.
  • has knowledge of model optimisation techniques, including hyperparameter tuning and principles of explainable AI.
  • has knowledge of deep learning techniques and architectures.
  • can assess his/her own work in relation to the hardware requirements for AI and ML applications, including computational needs for deep learning.
  • is familiar with Python libraries useful for model development.
  • understands the principles of network theory and graph theory and their applications in AI-driven solutions.
Skills

The candidate...

  • can apply knowledge to develop and present a portfolio demonstrating practical experience with AI solutions.
  • can explain his/her choices for implementing and optimizing machine learning models using various Python libraries, and explain the rationale behind design and optimisation choices.
  • can apply knowledge to design and develop recommender systems utilising AI-based techniques.
  • can reflect on his/her own application of network and graph theory in AI problem-solving scenarios.
  • can apply and critically reflect on network and graph theory in AI problem-solving scenarios.
  • can explain his/her choices in tuning models effectively using optimization techniques and ensure interpretability through Explainable AI methods.
  • can find, prepare and manage data for deep learning models, ensuring data quality, consistency, and ethical handling.
General Competence

The candidate...

  • can evaluate and critically assess AI models and their real-world applications, reflecting on their impact, limitations, and reliability.
  • understands and applies ethical and societal principles when developing AI systems, demonstrating responsibility for transparency, fairness, and data integrity.
  • can exchange points of view with others with a background in the trade/discipline and participate in discussions about the development of good practice, contributing to team-based decision-making and peer learning in AI projects.
  • can communicate technical results clearly and effectively to colleagues, clients, and stakeholders with varying levels of technical expertise.
  • can continuously update his/her vocational knowledge and adapt to emerging tools, frameworks, and methods in AI and deep learning.
  • can plan and carry out vocational tasks and projects alone or as part of a group and in accordance. with ethical requirements and principles, applying best practices in AI model development and optimisation.
  • can contribute to organisational development by implementing efficient, ethical, and innovative AI solutions that align with professional and societal standards.
Learning Activities

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.

Reading List

Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.