Overview:
- Credits:
- 10.0
- Level:
- 4
- Semester:
- Summer
- Subject:
- Management Information Systems
- School:
- Business
- Coordinator:
- Mr Allen Higgins
MIS41230 Machine Learning for Business
Academic Year 2022/2023
Machine Learning for Business: a hands-on approach will introduce you to current commercial/industry Machine Learning practices. The module gives you the opportunity to develop a range of skills and knowledge of the technologies and business applications of Machine Learning, a central technology in the recent advances in Artificial Intelligence. Classes employ practical experimentation and reflection using Python and current open source tools and public domain-data resource based on ‘Hands-On Machine Learning with Scikit-Learn and TensorFlow’ (Geron, 2019).Show/hide contentOpenClose All
Curricular information is subject to change
Learning Outcomes:
On completion of this module, students will have: Knowledge of the main ideas and techniques of Machine Learning; Knowledge and understanding of a range of industry settings in which firms deploy machine learning tools and related frameworks used in these contexts. You will be able to: Identify Machine Learning techniques relevant to specific business contexts and data; Research an unfamiliar industry or firm context in order to establish the nature of an analytical problem; Formulate a machine learning plan and comment critically on machine learning strategies adopted by others; Explain both findings and the strengths and weaknesses of the methods used to arrive at these findings.
Lessons will refer to the book “Hands-On Machine Learning with Scikit-Learn and TensorFlow”.
We identify business cases and adapt provided code to analyse these datasets.
| Student Effort Type | Hours |
|---|---|
| Lectures | 30 |
| Specified Learning Activities | 60 |
| Autonomous Student Learning | 110 |
| Total | 200 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
| Remediation Type | Remediation Timing |
|---|---|
| In-Module Resit | Prior to relevant Programme Exam Board |
Feedback Strategy/Strategies
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
How will my Feedback be Delivered?
Formative feedback is offered during tutorials in class.
| Name | Role |
|---|---|
| Claire Hughes | Lecturer / Co-Lecturer |
| Dr Yossi Lichtenstein | Lecturer / Co-Lecturer |
| Dr Linus Wunderlich | Lecturer / Co-Lecturer |
Discover our Rankings and Accreditations
