MM257: Introduction to Machine Learning

Module code: MM257

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: 5

When you'll be taught: Semester 1

Module convenor: Dr Giannis Haralabopoulos, email: i.haralabopoulos@henley.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE MM1F28 (Compulsory)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: No placement specified

Academic year: 2024/5

Available to visiting students:

Talis reading list:

Last updated: 19 November 2024

Overview

Module aims and purpose

Data-driven processes are becoming increasingly popular amongst organisations; quickly replacing qualitative assessments that were, until recently, based on experience and tacit knowledge. Machine learning is widely used in industry and business applications to provide recommendations, make predictions, or extract knowledge. A good understanding of machine learning has, therefore, become a fundamental skill for anyone looking to work with organisation that plan to make strategic use of their data. In this module students will be introduced to key concepts related to machine learning and will become adept at managing and analysing data. Furthermore, students will gain experience with building predictive models that can lead to data-driven solutions. The workshops will provide students with the opportunity to develop programming skills using a state-of-the-art tool with Python programming language.

The aim of this course is to provide students with the skills and knowledge required to manage and analyse data, towards developing state of the art predictive models that lead to data-driven business solutions.

To satisfy this general aim, students will acquire key knowledge and skills in:

• Python Coding
• Machine Learning Theoretical Concepts
• Accessing, storing, and handling univariate and multivariate data
• Machine Learning Applications
• Machine Learning Classification and Prediction
• Natural Language Processing
• Image Processing and Analytics

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Identify and apply appropriate Machine Learning methods to analyse and predict data.
2. Demonstrate effective use and evaluation of Python based Machine Learning methods and models.
3. Develop and deploy Python Scripts to present and critically assess Machine Learning results.
4. Analyse a data related problem and propose appropriate Machine Learning approaches that will support data-driven decision making.
5. Work with PyCharm development tool and have familiarity with Python coding tools.

Module content

1. Machine Learning Basics
2. Perceptron and Features
3. Margin Maximization and Regression
4. Neural Networks and Convolutional Neural Networks
5. State Machines and Markov Decision Process
6. Reinforcement Learning and Recurrent Neural Networks
7. Visualizations and Key Concepts
8. Natural Language Processing and Text Classification
9. Image Analysis and Classification

Structure

Teaching and learning methods

This module will be a combination of lectures and practical workshops that will enable students to acquire key concepts and practical skills in Machine Learning. It requires prior knowledge and experience in data analytics, as taught in MM1F28.

Data sets related to business and real-life problems will be provided as ‘case studies’ to students, who will then have to apply everything they learned to develop and evaluate Machine Learning applications.

Study hours

At least 20 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

To pass the module, students must demonstrate satisfactory understanding of Machine Learning concepts as well as demonstration of basic use of Python based tools for Machine Learning and evaluation of results.

For Merit level performance, students must demonstrate competence in formulating Machine Learning solutions through suitable application of tools and critical evaluation of results.

For Distinction level performance, students must demonstrate a high level of competence in critical formulation of Machine Learning solutions and a highly competent application of appropriate tools and critical analysis when evaluating and interpreting the results. 

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Post-workshop, students will be presented with a range of practical exercises based on the workshop dataset. They are encouraged to work on these task and report their results in the next workshop.

Reassessment

Additional costs