MACHINE LEARNING & VISUALISATIONS - 2019/0
Module code: MANM354
Machine Learning & 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
GARN Wolfgang (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: I460
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
The module content will focus on a selected set of critical areas in Machine Learning. As an indication of the kind of issues that will be covered, please find below an indicative set of topics.
- Statistical Learning
- Classification (e.g. Logistic Regression)
- Tree base methods (e.g. random forest, decision trees)
- Support Vector Machines
- Unsupervised Learning (e.g. k-means clustering)
- Meta Heuristics (e.g. genetic algorithms, ANN, deep learning)
|Assessment type||Unit of assessment||Weighting|
|Examination||2 HOUR EXAMINATION (CLOSED BOOK)||50|
In order to achieve the threshold standard for the award of credits for this module, the student must meet the following criteria related to the learning outcomes:
- apply the theories, conceptual frameworks and methodologies that underpin machine learning;
- prove the ability to visualise data and business insights;
- demonstrate evidence of background reading and research of the academic and practitioner literature relevant to Machine Learning.
Thus, the summative assessment for this module consists of an exam and a coursework.
The 2-hour examination (50% of the total mark) will be a standard closed book examination, with material coming directly from the module content. The broader topics for the questions will be covered during the lectures and laboratory sessions. The problems will be similar to those discussed in the lectures, computer laboratory or indicated in the essential reading material. The answers will mainly be in form of mathematical and algorithmically workings, and will be in alignment with the criteria related to learning outcomes (1).
For the coursework (50% of the total mark), the students will individually apply and deepen their knowledge with the criteria related to learning outcomes (1) to (3) mentioned above. The answers will include the analysis of data. The analysis will use machine learning techniques, creating algorithms and visualising the results appropriately. Furthermore, evidence of background reading has to be provided. Students will be informed about the coursework topic in the beginning of the semester.
Formative assessment will be provided throughout the course. Students will demonstrate the ability to analyse and visualise business. They will be given business problems on a weekly basis. Machine Learning techniques will be used for gaining business insights. Formative feedback on the student’s approach will be given during the computer laboratory sessions.
- To gain business insights using Machine Learning and Visualisations
|001||Analyse, classify and visualise data;||KPT|
|002||Explain and apply supervised & unsupervised learning;||CPT|
|003||Give insights into business analytic challenges;||CPT|
|004||Present results to management for decision making.||CPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 106
Lecture Hours: 22
Laboratory Hours: 22
Methods of Teaching / Learning
The teaching and learning 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.
The teaching and learning methods include: a lecture every week as well as several student exercises. Web-based learning support and electronic resources will be provided.
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.
Reading list for MACHINE LEARNING & VISUALISATIONS : http://aspire.surrey.ac.uk/modules/manm354
Programmes this module appears in
|Business Analytics MSc||2||Compulsory||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 2019/0 academic year.