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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 |
---|---|
Lectures | 30 |
Specified Learning Activities | 60 |
Autonomous Student Learning | 110 |
Total | 200 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Essay: Essay responding to a business problem and dataset | Varies over the Trimester | n/a | Graded | Yes | 100 |
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.
Name | Role |
---|---|
Claire Hughes | Lecturer / Co-Lecturer |
Dr Yossi Lichtenstein | Lecturer / Co-Lecturer |
Dr Linus Wunderlich | Lecturer / Co-Lecturer |