| Unit name | Fundamentals of AI |
|---|---|
| Unit code | COMSM0170 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Burghardt |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Computer Science |
| Faculty | Faculty of Science and Engineering |
Why is this unit important?
This foundational unit is designed to introduce students from diverse academic backgrounds to the discipline of artificial intelligence, that is it offers a systematic introduction to Python programming, enabling students to manage the workflow from data ingestion to model development. The unit covers both classic learning algorithms, as well as introductory concepts in neural networks and deep learning, the unit builds essential skills for solving real-world business problems such as classification and prediction. In addition, the course integrates hands-on practice with critical reflections on the ethical implications of AI, fostering both digital literacy and a sense of responsibility. As the first unit in the programme, it provides the essential technical and conceptual foundation for subsequent modules and the final project.
How does this unit fit into your programme of study
As the programme’s entry-level course, Fundamentals of AI is specifically designed for students with no prior technical background. It offers an introduction to AI systems, core algorithms, and Python programming. Through a structured 10-week progression, students will develop the technical competencies required for future modules—including AI deployment, business applications, and entrepreneurial projects—as well as for undertaking independent research and data-driven dissertations. Serving as a critical bridge between theory and practice, this unit enables students to build a robust base for advanced AI studies across academic and professional settings.
An overview of content
The first part of the course focuses on Python programming fundamentals, alongside essential data analysis libraries. The second part of the unit explores the core topics of machine learning, and introductory deep learning models (i.e. neural networks). Each week includes lectures and practical lab sessions using Jupyter Notebooks, where students implement models on real-world datasets. The course culminates in a final applied project that integrates problem formulation, algorithm design, and result interpretation.
How will students, personally, be different as a result of the unit
Upon completing this unit, you will have developed both technical skills and analytical thinking regarding the construction of AI components. You will be capable of independently conducting data processing, implementing algorithms, and evaluating outcomes. You will also learn to explain concepts and Python code that underpins AI models and translate complex technical concepts into accessible language for non-technical audiences. By engaging with topics such as model limitations and ethical risks, you will enhance your critical thinking and digital responsibility, emerging as AI practitioners with both technical acumen and interdisciplinary awareness.
Learning Outcomes
By the end of the unit, you will be able to:
The unit adopts a blended teaching approach that combines structured lectures with hands-on practical work. Each week includes two hours of lectures where instructors deliver in-depth explanations of Python fundamentals, machine learning principles, and common AI algorithms, helping students build a coherent knowledge base. This is complemented by two hours of lab sessions using Jupyter Notebooks, where students apply what they’ve learned through coding exercises on real datasets—covering the full workflow from data preprocessing to model evaluation.
Learning is further supported by two summative tasks: a group project presentation and an individual coding assignment. Both tasks are introduced early and progressively developed throughout the unit. Students are encouraged to experiment, iterate, and apply their growing technical knowledge in solving real-world business problems. The unit also incorporates a formative feedback system, through coding tutorials, debrief discussions, and peer review activities, ensuring students receive continuous guidance and can refine both their theoretical understanding and implementation skills over time.
Tasks which help you learn and prepare you for summative tasks (formative):
To support students from diverse academic backgrounds—particularly those new to programming or AI—this unit integrates two key formative components to build confidence and bridge theory with practice:
Tasks which count towards your unit mark (summative):
The unit includes two summative assessment components:
Students work in small teams to address a practical business problem using one or more AI methods taught in the course. In Week 10, each group presents their solution, covering problem framing, model selection, implementation, results, and reflections. Assessment focuses on clarity, technical justification, teamwork, and relevance to business needs.
Students independently complete a coding task using Python, implementing a simple AI model based on real-world data. They submit a structured Jupyter Notebook demonstrating data preparation, model development, performance evaluation, and reflective commentary. This task assesses the student’s ability to apply core machine learning techniques and explain their solution clearly.
When assessment does not go to plan
Students who are unable to complete or fail a summative assessment will be reassessed as follows:
All reassessments test the same intended learning outcomes. No new groups will be formed. Extensions and deferrals follow university policy.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMSM0170).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
