MIS41230 Machine Learning for Business

Academic Year 2020/2021

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).

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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.

Indicative Module Content:

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.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Essay: Essay responding to a business problem and dataset Varies over the Trimester n/a Graded Yes

100


Carry forward of passed components
Yes
 
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
Dr Yossi Lichtenstein Lecturer / Co-Lecturer
Dr Linus Wunderlich Lecturer / Co-Lecturer
Mr Stephen Keenan Tutor
Professor Stefan Klein Subject Extern Examiner

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