Module Specification

Applied Machine Learning

Module Specification

Applied Machine Learning: Transform Data into Insights with Precision


Making sense of complex data requires more than intuition; it requires precision, techniques, and the discipline to apply them critically. In this module, we emphasise the foundation of machine intelligence, focusing on how algorithms interpret real-world scenarios. Studying phenomena through statistical learning creates a robust framework for understanding the diverse applications of data science.

This module delves into the principles of model construction and evaluation. You will learn about supervised and unsupervised learning, gain hands-on skills with algorithms such as linear regression and decision trees, and explore advanced topics like ensemble methods and hyperparameter tuning. Python-based programming with industry-standard libraries like scikit-learn and pandas will enhance your practical capability in data manipulation and visualization.

By engaging with this module, you acquire the skills to transform raw data into meaningful insights. Empower yourself with the ability to handle intricate datasets, engineer impactful features, and reduce dimensions to boost model performance. With a focus on real-world problems from healthcare to finance, you will gain confidence in deploying solutions that matter, laying the groundwork for further exploration into deep learning and beyond.


Mode(s) of Study Code CATS Credits ECTS Credits Framework HECoS code
Full-time Blended Learning
Part-time Blended Learning		 ML71 30 15 FHEQ - L7 machine learning (100992)

Prerequisites and Co-requisites

None

Learning Outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Demonstrate comprehensive understanding of machine learning algorithms including supervised and unsupervised learning techniques and their applications.
LO2 Intellectual Skills Critically analyse the effectiveness of various machine learning models and techniques in dealing with complex datasets.
LO3 Intellectual Skills Evaluate and formulate strategies to improve model performance through dimensionality reduction and hyperparameter tuning.
LO4 Technical/Practical Skills Develop and implement machine learning models using industry-standard tools like Python, scikit-learn, and pandas.
LO5 Professional/Transferable Skills Adhere to ethical guidelines and demonstrate professional responsibility in handling data and sharing results within machine learning projects.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 Fundamentals of Machine Learning
Discover the core principles and motivations behind machine learning to understand how and why machines learn from data. Explore the differences between supervised and unsupervised learning to grasp how algorithms classify, predict, and identify patterns. This foundation will equip you with the conceptual framework necessary to approach complex datasets and devise appropriate machine learning strategies.
Week 3 Data Preprocessing and Cleaning
Learn essential techniques for preparing real-world data for analysis, focusing on the importance of data quality. Master methods like handling missing data, normalization, and standardization to ensure your datasets are accurate and suitable for model training. Understanding data preprocessing is crucial for building reliable models and dealing with the messiness inherent in real-world datasets.
Week 4 Supervised Learning Algorithms
Dive into key supervised learning algorithms such as linear regression, logistic regression, KNN, SVM, and decision trees. Learn how to apply these algorithms to make predictions and classifications based on labelled data. Developing proficiency with these methods will allow you to tackle a wide range of predictive modelling tasks.
Week 5 Unsupervised Learning and Dimensionality Reduction
Explore unsupervised learning techniques to uncover hidden patterns in unlabelled data. Focus on dimensionality reduction methods like PCA and t-SNE to simplify high-dimensional datasets while retaining their essential structures. Understanding these techniques will enable you to manage complex datasets and extract meaningful insights.
Week 6 Feature Engineering and Selection
Enhance model performance and interpretability by mastering feature engineering and selection techniques. Learn methods such as recursive feature elimination and regularization techniques like Lasso and Ridge regression. This knowledge is essential for identifying the most influential variables and reducing model complexity.
Week 7 Model Evaluation and Validation
Understand the importance of assessing model performance through robust evaluation techniques. Learn to apply k-fold cross-validation and interpret performance metrics like accuracy, precision, recall, F1 score, and AUC-ROC. This will enable you to critically evaluate models and ensure they generalize well to new data.
Week 8 Hyperparameter Tuning and Optimization
Optimize your machine learning models by mastering hyperparameter tuning methods. Use grid search and random search strategies, leveraging tools like GridSearchCV and RandomizedSearchCV. Fine-tuning hyperparameters is key to enhancing model performance and achieving the best predictive results.
Week 9 Ensemble Methods
Discover how to improve predictive performance using ensemble methods. Study bagging and boosting techniques such as Random Forest, AdaBoost, and Gradient Boosting Machines. Combining multiple models will give you powerful techniques to handle complex predictive tasks with greater accuracy.
Week 10 Foundations for Deep Learning
Prepare for advanced topics in deep learning by understanding the basic concepts of neural networks and deep architectures. Explore how these build upon machine learning foundations to handle even more complex data patterns. This groundwork will set the stage for studying frameworks like TensorFlow and PyTorch, enabling you to expand your machine learning capabilities.

References/Indicative Reading List

Importance ISBN Description
Core Textbook 9781681089416 Chatterjee, Indranath. Machine Learning and Its Application: A Quick Guide for Beginners. Bentham Science Publishers, 2021
Core Textbook 9781838820299 Bonaccorso, Giuseppe. Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work. Packt Publishing, 2020
Supplementary Reading 9783110550320 Bhattacharyya, Siddhartha and others. Machine Learning for Big Data Analysis, De Gruyter, 2018
Supplementary Reading 9783030181130 Forsyth, D. Applied machine learning. Cham: Springer International Publishing, 2019
Supplementary Reading 9781492098058 Prosise, J. Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically. 1st Edition: O'Reilly Media, 2022
Supplementary Reading 9780262542524 Alpaydin, Ethem. Machine learning. MIT press, 2021.
Supplementary Reading 9780198828044 Trappenberg, Thomas P. Fundamentals of machine learning. Oxford University Press, 2019
Supplementary Reading 9781784399689 Gollapudi, Sunila. Practical machine learning. Packt Publishing Ltd, 2016
Supplementary Reading 9781800567689 Masood, Adnan. Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Packt Publishing, 2021

Student Workload

The methods of teaching and learning for this module are based on the School's Technical 30 teaching system, consisting of the following activities.

Activity Total hours
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

 1.50
Concept learning (knowledge graph)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.

 36.00
AI formative assessment

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.

 18.00
Workshop/Lab Sessions

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

 27.00
Independent reading, exploration and practice

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

 153.50
Summative assessment

Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module.

 64.00
300.00

Assessment Patterns

Weighting Format Outcomes assessed
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.		 K LO1
I LO2
I LO3
T LO4
P LO5
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.		 K LO1
I LO2
I LO3
P LO5

Programmes this module appears on

Programme Term Type
1 MSc AI and Machine Learning 1 Core
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Applied Machine Learning: Transform Data into Insights with Precision (ML71)