Module Specification

Applied Machine Learning 1

London school of INNOVATION

Module Specification

Applied Machine Learning 1


The module will cover the theoretical and applied concepts of machine learning. The history of machine learning will also be considered. Different machine learning paradigms including supervised, unsupervised, and reinforcement learning will be looked at. This includes the underlying algorithm in each paradigm, including regression, kernel methods, clustering algorithms, and neural networks. Decision trees in machine learning will be considered, as well as random decision forests. Emerging technologies, such as generative AI, will form part of the module. Naive Bayes and Gaussian Mixture Models will be explored as examples of Generative ML models. Support vector machines will be looked at too.

There will be a session on evaluating machine learning algorithms, as well as evaluating training processes and model selection. Students will look at how to train neural networks  and look at reinforcement learning. There will be an introduction to MLOps. Methods for solving machine learning problems in low data-availability cases will be looked at, and several methodologies for dealing with such issues. The course covers tools and best practices for creating industry-level machine learning pipelines. Through rigorous examples and case studies, data preparation, algorithm selection, training and model evaluation, and monitoring will be brought to attention. A session will also look at students’ professional development strategy.


Code Number of Credits ECTS Credits Framework HECoS code
ML71 30 15 FHEQ - L7 machine learning (100992)

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Comprehensive awareness of the structure and impact of algorithms within the machine learning paradigms.
LO2 Intellectual Skills Critically compare machine learning algorithms through rigorous evaluation to achieve generalisation.
LO3 Intellectual Skills Synthesise knowledge of various machine learning models to conceptualise and address real-world data-driven questions.
LO4 Technical/Practical Skills Demonstrate skills and self-direction in applying suitable machine learning techniques, such as kernel methods and neural network architectures, for problem-solving.
LO5 Technical/Practical Skills Design machine learning models using supervised, unsupervised, and reinforcement learning approaches.
LO6 Professional/Transferable Skills Devise professional development plans in machine learning expertise to advance knowledge, understanding, and skills.

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
T LO5
P LO6
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 LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 36.00
AI formative assessment 18.00
Workshop/Lab Sessions 27.00
Independent reading, exploration and practice 153.50
Summative assessment 64.00
300.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Machine Learning Paradigms The chapter presents an in-depth exploration of machine learning and its various paradigms, including supervised, unsupervised, and reinforcement learning, providing foundational knowledge of the algorithms that animate them. Through this lens, students will gain a holistic view of the machine learning field and its applications, preparing them to comprehend and differentiate between the approaches and their usages.
Week 2 Regression Analysis and Kernel Methods Focusing on regression and kernel-based learning strategies, this chapter equips students with robust analytical tools to model and interpret complex data. By investigating data patterns and making predictive judgments, students will learn to apply these methods in solving real-world problems, thereby solidifying their understanding of these vital machine learning techniques.
Week 3 Clustering Algorithms This chapter dives into unsupervised learning through the study of clustering algorithms, uncovering the many ways machine learning finds patterns within unlabelled data. It emphasises the importance of these algorithms in identifying natural groupings in data, a critical skill for areas with vast, undocumented datasets.
Week 4 Decision Trees and Random Forests Addressing the powerful tools of decision trees and random forests, this chapter merges theoretical principles with practical applications, illustrating how these methodologies contribute to insightful data analysis and robust prediction models, and are fundamental to the machine learning toolkit.
Week 5 Generative Learning Algorithms Generative machine learning models, including Naive Bayes, GMM, and Generative Adversarial Networks, are the focal point of this chapter, which delves into the world of algorithmic content generation, providing insights into their capacity for modelling and synthesis, essential for understanding AI creativity.
Week 6 Support Vector Machines (SVMs) and Kernel Trick Support Vector Machines (SVMs) and the kernel trick take centre stage, introducing another dimension to machine learning classification problems. The power of SVMs in optimising decision boundaries and dealing with higher-dimensional data is unpacked, extending students' capabilities in binary classification.
Week 7 Neural Networks Here, students traverse the domain of neural networks and deep learning, from early perceptrons to state-of-the-art architectures, covering essential concepts such as network layers, activation functions, and backpropagation, setting the scene for understanding the profound impact of these models.
Week 8 Algorithms and Optimising Models A critical analysis of machine learning algorithms is essential, and this chapter provides the methodologies for evaluating and fine-tuning models to ensure that they generalise beyond their training data, which is an indispensable skill for any machine learning professional.
Week 9 MLOps and Model Deployment To convey the practicality of machine learning in industry, this chapter introduces MLOps, combining machine learning with operations to streamline and scale model deployment. It encapsulates best practices for sustainable machine learning workflows, optimisation, and maintenance.
Week 10 Professional Development Culminating the module, this chapter encourages students to reflect on their professional development within applied machine learning. It paves the way for constructing a learning strategy that aligns with their career objectives, fostering continuous advancement and innovation in their future endeavours.

Module References

Type Description
Video All, but the neural network portion of the course is covered by this book. Each topic comes with a unique problem to be solved with the algorithm said in the topic.
These include: Regression, Classification, Support Vector Machines, etc.
Book Forsyth, D. (2019). Applied machine learning. Cham: Springer International Publishing.
Book Prosise, J. (2022). Applied Machine Learning and AI for Engineers: Solve Business Problems That Can't Be Solved Algorithmically 1st Edition: O'Reilly Media.
Video Python for Deep Learning — Build Neural Networks in Python [Video]
The book provides details for implementing neural networks with python.

Methods of teaching/learning


Introductory lecture (1.50 hours)

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.


Concept learning (knowledge graph) (36.00 hours)

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.


AI formative assessment (18.00 hours)

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.


Workshop/Lab Sessions (27.00 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, 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. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (153.50 hours)

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.


Summative assessment (64.00 hours)

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.

Programmes this module appears on

Programme Term Type
1 AI and Machine Learning (MSc) 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 1 (ML71)