2019/20 Taught Postgraduate Module Catalogue
LUBS5990M Machine Learning in Practice
15 creditsClass Size: 200
Module manager: Xingjie Wei
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2019/20
This module is not approved as an Elective
Module summaryThis module gives students a solid understanding of machine learning methodologies and the chance to use them in practice within a business context. Students will learn about classical machine learning approaches such as decision trees, probability-based classification, support vector machines and neural networks. It will also cover bleeding edge methods such as deep learning and text analytics. This module assumes no prior knowledge of machine learning and would be suitable for students with strong quantitative skills in disciplines such as business, science, engineering, maths, computer science, geography etc.
ObjectivesThis module aims to give students a sound grounding in machine learning methodologies at the level necessary to allow them to use and interpret machine learning methods in a business environment.
On completion of this module, students will be able to use machine learning to analyse data, and be able to critically evaluate their potential and pitfalls, including the following methods and concepts:
1. Understand and utilise regression and regression trees.
2. Understand and utilise naïve Bayes classification.
3. Understand and utilise support vector machines.
4. Understand and utilise neural networks and deep learning.
5. Able to apply machine learning for text analytics.
Students will learn to critically evaluate machine learning methods for business problems and expand upon their analytical thinking skills.
- Decision trees
- Logistic regression
- Regression trees
- Probability and naïve Bayes classification
- Support vector machines
- Neural networks
- Deep learning
- Text mining / analytics
An understanding of the methods is provided in lectures along with a computer practical where students learn to use the methods in practice.
|Delivery type||Number||Length hours||Student hours|
|Private study hours||125.00|
|Total Contact hours||25.00|
|Total hours (100hr per 10 credits)||150.00|
Private studyPost-lecture reading: 50 hours
Seminar reading and preparation: 40 hours
Examination revision: 35 hours
Opportunities for Formative FeedbackTeaching staff will provide formative feedback on the development of the 3,500 word report students submitted in the end of the semester, in the terms of presentation, knowledge and understanding, and analysis and evaluation.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Report||3,500 word report||100.00|
|Total percentage (Assessment Coursework)||100.00|
Report is based on utilising machine learning methods on a specific dataset (dataset changes annually). Resit is by 3,500 word report for 100% of the module mark.
Reading listThere is no reading list for this module
Last updated: 30/04/2019
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