MACHINE LEARNING/AI AND VISUALISATIONS - 2024/5
Module code: MANM547
Machine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
Surrey Business School
FU Colin (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 90
Lecture Hours: 11
Laboratory Hours: 22
Guided Learning: 12
Captured Content: 15
Prerequisites / Co-requisites
- Statistical Machine Learning
- Classification (e.g. KNN, Naïve Bayes, Decision Trees, Classification rule Learners)
- Numerical Prediction (e.g. Linear Regression, Regression Trees, and Model Trees)
- Support Vector Machines
- Meta Heuristics (e.g. genetic algorithms, ANN, deep learning)
- Pattern Detection (e.g. Association Rules)
- Clustering (e.g. K-Means Clustering)
|Unit of assessment
|Business Analytics Report
The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to apply Machine Learning and AI models on any business problems for actionable insight while telling the data story using effective visualisation. Students will demonstrate the ability to analyse and visualise business. Machine Learning techniques will be used for gaining business insights. Student(s) must apply business analytics to a chosen business problem which the topic(s) will be selected in the beginning of the semester. In order to achieve the threshold standard for the award of credits for this module, it is expected that the student will undertake background reading and research of the academic and practitioner literature relevant to Machine Learning.
Thus, the summative assessment for this module consists of:
- Business Analytics report (addresses all learning outcomes)
Students are presented with business problems on a weekly basis. Students will have to demonstrate the ability to analyse and visualise business using Machine Learning techniques to gain business insights.
Feedback on student's approach of business problems will be given during the computer laboratory sessions. Solutions and recommendations will need to be given with rationale.
- Facilitate a comprehensive understanding of the various machine learning models
- Enable students to evaluate machine learning models comparing Sensitivity, Specificity and Accuracy.
- Equip students with the skills to apply each suitable models effectively with real data in business context for intelligence gathering and decision making
|Demonstrate an understanding of different machine learning and AI principles and data analysis
|Apply variety of data analytics techniques, from Machine Learning, AI and Data Mining, for data analysis through the whole data-process-model lifecycle
|Write programming codes to solve business-related problems to achieve optimality in performance in terms of Sensitivity, Specificity and Accuracy.
|Analyse some non-trivial problems in real-world applications, understand the concepts behind them and come up with possible algorithmic solutions with the capability of interpreting the solutions in business context.
|Present actionable insights based on the obtained intelligence for decision making with effective visualisation and story-telling techniques
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to cultivate an understanding of the main issues and challenges provide a coherent conceptual framework develop a critical awareness of the various approaches of machine learning and AI. The teaching and learning methods include: a lecture, laboratory session, captured contents and guided learning every week as well as take home activities. Web-based learning support and electronic resources will also be provided.
The module will develop analytical skills and the understanding of the subject area through:
- In-class discussion
- Independent learning
- Guided learning
- Captured content
The module will develop practical skills through:
- Lab sessions
- Peer discussion
- Independent learning
- Guided learning
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MANM547
Employability: students develop highly-sought machine learning and AI skills which they apply in a range of different business contexts.
Global and Cultural Capabilities: This module offers students to solve machine learning and AI challenges without geographical restrictions.
Digital Capabilities: Throughout the module, students learn to navigate and use R and/or Python Programming to analyse data in business context. The analysis will use machine learning and AI techniques going through CRISP-DM cycle (or equivalent), creating algorithms and visualising the results appropriately to tell data story.
Resourcefulness & Resilience: students demonstrate capability to either work on their own or in a team to prepare a substantial piece of coursework which will involve initiative, challenges and opportunities to demonstrate their creativity and an ability to adapt data analysis based on the chosen business context.
Sustainability: The module aims at developing students¿ understanding, awareness, and capability to develop innovative and sustainable solutions using machine learning and AI techniques for actionable insights.
Programmes this module appears in
|Business Analytics MSc
|A weighted aggregate mark of 50% is required to pass the module
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.