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2019/20 Taught Postgraduate Module Catalogue

LUBS5990M Machine Learning in Practice

15 creditsClass Size: 200

Module manager: Xingjie Wei
Email: X.Wei1@leeds.ac.uk

Taught: Semester 2 View Timetable

Year running 2019/20

This module is not approved as an Elective

Module summary

This 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.

Objectives

This 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.

Learning outcomes
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.


Syllabus

Indicative content:
- 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.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Practicals101.5015.00
Lecture101.0010.00
Private study hours125.00
Total Contact hours25.00
Total hours (100hr per 10 credits)150.00

Private study

Post-lecture reading: 50 hours
Seminar reading and preparation: 40 hours
Examination revision: 35 hours

Opportunities for Formative Feedback

Teaching 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


Coursework
Assessment typeNotes% of formal assessment
Report3,500 word report100.00
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 list

There is no reading list for this module

Last updated: 30/04/2019

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