MIS41230

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 Hours:

Approaches to Teaching and Learning:

Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles

Assessment Strategy

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.