- Management Information Systems
- Mr Allen Higgins
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Curricular information is subject to change
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|
|Specified Learning Activities||
|Autonomous Student Learning||
Not applicable to this module.
|Remediation Type||Remediation Timing|
|In-Module Resit||Prior to relevant Programme Exam Board|
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
Formative feedback is offered during tutorials in class.
|Dr Yossi Lichtenstein||Lecturer / Co-Lecturer|
|Dr Linus Wunderlich||Lecturer / Co-Lecturer|
|Mr Stephen Keenan||Tutor|
|Professor Stefan Klein||Subject Extern Examiner|