Open Course Materials

FML: Machine Learning in Financial Services

aburford — Thu, 18 Dec 2025 15:09:45 +0000

FML: Machine Learning in Financial Services aburford Thu, 18/12/2025 - 15:09

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ACP: Applied Cloud Programming

aburford — Tue, 18 Nov 2025 14:11:51 +0000

ACP: Applied Cloud Programming aburford Tue, 18/11/2025 - 14:11

Welcome to Applied Cloud Programming!

ACP is intended as a purely practical contemporary course which equips you with the knowledge to identify, use and apply cloud technology in individual software development projects.

Our main goal is to provide you with the necessary skills to thrive in the modern cooporate world with a lot of background information and insights.

What will you learn:

How to write a REST-service in Java using Spring Boot
The cloud providers and their offerings
docker / docker image / container / virtual machine
Service-orientied vs Monolith
A bit about Kubernetes (k8s)
How to use docker and docker images to run everything
How to consume and process JSON data
How to interact with other services using http-conversations
Service-oriented architectures
Messaging (Kafka, RabbitMQ, SQS)
Databases (PostgreSQL, DynamoDB)
Caching
Serverless computing (Lambdas)

The programming language we use is Java (that is mandatory) and the framework Spring, yet everything can be applied to any other modern language as well. We will show examples in other languages (Golang, Rust, Python) as well, yet Java is set as submission language.

The course contents are spread out in the indvidual lecture documents (will be filled over time in the schedule document) and the recordings.

The schedule of the lectures, tutorials, etc can be found here.

Course structure

We will be delivering the course via lectures, some tutorials online, and a chat forum (piazza).

Please be aware that this course requires a lot of your own reading of materials which can be found in the resource list. The most relevant titles are:

Should you only read one book then this one: https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/citation/60057773370002466?auth=SAML
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/citation/56103033230002466?auth=SAML
https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/citation/56103033160002466?auth=SAML
Extremely well written book about the general concepts: https://eu01.alma.exlibrisgroup.com/leganto/public/44UOE_INST/citation/56103033180002466?auth=SAML

The team

ILP will be delivered by 1 lecturer (Dr Michael Glienecke - course organizer) and 2 TA:

Haleema Sadia
Cameron Wheeler

Both will have one (very interesting and advanced) lecture each and support you on piazza, Q&A, etc.

The assessments

This year we will try something unconventional to spice up the course and (hopefully) make it much more fun for you.

Instead of the usual essay, we are adding a "free style" part where you can develop whatever you want to - as long as it is somehow related to the ILP topic - and defend what you have done in a video presentation. Some rules will apply to that and the marking will be rubrics based, giving you a very large area to express you and your ambitions as you like.

Details are about to follow at a later time, yet the main intention is to make this so much more tangible and joyful for you!

Saing this, to get a real good mark will require a considerable amount of work and contribution, within reasonable limits.

For those of you who face special challenges, please let us discuss how we can mitigate. We do have many ideas, but as this is a very personal issue, many will prefer more custom solutions.

Lecture recordings

All lecture recordings should be accessed via Learn; you will need to log in using your EASE account. (Learn provides you with access to any lecture recordings available for this course. You will need to select the "lecture recording" link once, before you can access any direct links to a lecture recording.)

So, please enjoy and have a lot of fun

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DS: Distributed Systems

ypatel — Mon, 22 Sep 2025 11:23:14 +0000

DS: Distributed Systems ypatel Mon, 22/09/2025 - 12:23

Course summary/outline

A distributed system is broadly categorized as a collection or network of loosely coupled, autonomous computers that can communicate with each other and execute logically separate computations, though these may be related to concurrent computations on other nodes.

* The nodes are relatively loosely coupled.
* Each node is a self-contained autonomous computer with its own peripherals.
* The system can survive various categories of node and network failures.
* The nodes may execute logically separate computations, though these may be related to concurrent computations on other nodes.
* The system may be modeled as synchronous or asynchronous.

Distributed systems have become pervasive-many applications now require the cooperation of two or more computers-yet the design and implementation of such systems remain challenging and complex tasks. Difficulties arise from the concurrency of components, the lack of a global clock and the possibility of independent failure of components. Moreover designs must aim to provide inter-operability, transparency and autonomy.

The emphasis of this module is on gaining understanding of the principles and concepts that are used to design distributed systems and how network and communication facilities can be leveraged to achieve efficient distributed computing. We will cover the following topics

Introduction and overview - Need for distributed systems (Failures, consistency, delay, etc.)
Architecture & Communication - Scalability, Load balancing, Partitioning, RPC
Fault Tolerance - Failure models, Reliability, Recovery
Coordination - Ordering & Causality, Distributed transactions, Concurrency Control, Consensus/Agreement
Consistency & Replication - Epidemic algorithms, Consistency Models, Replica management
Distributed Storage - File systems, Large Scale systems
Issues - Energy/Power, Security, Local OS Support, Verification, Testing

Learning Outcomes

On completion of this course, the student will be able to:

Develop an understanding of the principles of distributed system and be able to demonstrate this by explaining them.
Being able to give an account of the trade-offs which must be made when designing a distributed system, and make such trade-offs in their own designs.
Develop practical skills of implementation of distributed algorithms in software so that they will be able to take an algorithm description and realize it in software.
Being able to give an account of the models used to design distributed systems and to manipulate those models to reason about such systems.
Being able to design efficient algorithms for distributed computing tasks.

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MLSP: Machine Learning Systems Project

aburford — Mon, 15 Sep 2025 06:01:01 +0000

MLSP: Machine Learning Systems Project aburford Mon, 15/09/2025 - 07:01

INTRODUCTION TO THE COURSE

The machine learning systems project is a research project that CDT ML Systems students will engage in over the course of the first year. The project itself will form a part of your PhD, but at the same time should be sufficiently well constrained to be able to report on it in the first year. The general process for the project will be to agree a preliminary exploration goal with the supervisor, followed by a project proposal, that should be agreed between student and supervisor by December 1 at the latest. Then the student undertakes the project and produces the "project report" for making a complete submission two weeks before the deadline. The deadline each year is at 4pm on May 1st (or 4pm the immediate Monday after if May 1st is on the weekend), so we expect a complete submission on April 23rd. Further edits can be made up to the deadline, but we do expect students to work and submit things early enough to cover eventualities - for example if you are ill near the deadline, that does not count as grounds for any special consideration.

STAGE 1: EXPLORATION

Every student should agree with their supervisory team a target problem and goal for their project, and an exploratory process to help firm up that project. Note this can be a team effort, and indeed will be better if it is! An explicit machine learning goal, and an explicit systems goal should both be articulated. This exploratory process should not just be a literature review (though that will be part), but it should also be some initial test coding, analysis and baselines. It should also identify the different contributions that different members of the team can make. The point of the exploratory process is to be able, by 1 December, to have a full project proposal for implementing the project, and confidence in the feasibility and likely success.

STAGE 2: PROJECT PROPOSAL

A project proposal should be written following the pro forma:

Title
Principal goal
Project description
Project evaluation mechanisms
Model paper: a paper that might form a good template for what is required for the work - i.e. the work extends this paper, or the work can follow a similar evaluatory framework
Completion criteria, and definition of success
If all goes well, the research contribution claim of the paper from this work
The minimal work to have some reasonable project writeup; the ease and expected time for achieving that
Risks and mitigations
Skills required, or training required and how it will be obtained in sufficient time
Resources required, and how they will be obtained
At least four key references

Note this is not required to be extensive; it should be focused. In reality students will need to establish more than just this pro-forma to be sure of the feasibility and likely success of the project, but this template provides a good guide of what should be considered. Where there is a Team of multiple students involved, it can be the same for each student but should identify the individualised contributions. There is every expectation that the overall goal of the project proposal will be defined by the supervisor; it is then the student's job to fill in the details above.

What makes a good project proposal?

A project proposal should target a well-defined problem. It should separate out the problem definition completely from the solution approach, it should have contingencies in place in case things don't work. There should be potential research novelty and contribution. There should be a vision for a publishable paper from the project.

Note that projects that don't require things to work are safer than projects where if things don't perform well, the student is left with nothing. A project of "Compare N things to identify what is best and why" doesn't depend on your `new method' delivering the goods. That said, it is perfectly acceptable to submit a project with negative results. It is just much harder to complete and to write up such a project. There is no requirement that "things work". Just a requirement that your approach and write-up is scholarly.

STAGE 3: DOING THE RESEARCH

The project involves significant work alongside the courses and other potential distractions. You will need to learn to balance time effectively. You should meet your supervisor regularly, either individually or as a Team, typically weekly or at least fortnightly (barring absences etc). Your supervisor should make time for you if you need a 1 to 1 with them - if you find the balance of a team is proving problematic for you, bring that up sooner rather than later. Your work is your own, your supervisor is your advisor, but in general you ignore supervisory advice at your peril.

One key target of a research project is to get to the critical point (the point where you have the results you need to write something good up) sooner rather than later. This then gives you time to improve the work, consolidate it, or engage in most risky further developments.

STAGE 4: WRITEUP

The main project write up will be written up in your preferred format. Preferably this would be the format of a conference or journal you might choose to submit to, if it is a decent length (e.g. 9 page) conference format. The write up will likely involve additional supplementary material to describe the work in further detail. Second there is a reflection component (typically 5-8 double spaced pages), which will satisfy the additional learning requirements of the project (more is required here for the longer 80 credit and 100 credit project. See the degree programme tables for more information and the SUBMISSION section below). In all cases this reflection will state:

A delimitation of the general field of the project and the longer term goals of the project.
A broader literature review covering the wider relevant contributions to this field than those covered in the project write up, along with why they are relevant
A summary of how the specific project fits within this broader scope
A detailed breakdown and description of the work done by different individuals and their contributions (when the work was done as a team or with collaboration with others)
A precise statement of both the ML and the Systems contributions of the project
A summary of the future goals and plan
IMPORTANT: A reflection on issues of responsible research and innovation
CRITICAL: For larger projects (80 or 100 credits), a reflection on the experience of collaboration and/or how cross ml-systems stack integration can be achieved with future collaboration. For an 100 credit course, please also propose on how one might manage any future ML-Systems collaboration effectively? Note these are course requirements for the longer projects, and so will be a marking criterion.

Note that the main project paper can be identical across different individuals if this was team work. The reflection however must be an individual document, written by the student and submitted as all their own work. Please be aware of the University plagiarism regulations. If in any doubt please discuss with the supervisor and/or the CDT directorship.

I recommend all students at least take a look at my Rules for Writing, and ideally follow them. I recommend buying and reading the recommended book as early as possible in the PhD.

STAGE 5: SUBMISSION

The student must submit one pdf document which is a merge of two (or three) documents into 1 pdf:

A description of the work in approximately 9 pages of conference format not including references or appendices, along with whatever appendices you wish, all in a single pdf. If your preferred conference format has a separate supplementary material, please just join the two pdfs into one
A reflection document, as above

For the purpose of the project, these documents will be marked on the basis of the scholarship, not on the basis of the research. Have you been rigorous? have you been comprehensive? Is your project and writing well organised? Have you done a good amount of work? Is your writing clear and unambiguous? Have you justified design decisions? Have you critiqued your work and the work of others? Have you considered the responsible research elements of the work? Is your work properly contextualised in the backgroun d literature, and in the future plan?

Separate from this, the writeup will also form a basis for the annual PhD review, along with an annual review meeting. The criteria there will be different. In that we will be looking for evidence for:

An ability to undertake research, and an understanding of what research means
An understanding of the point of the work, the direction of future work, and the more immediate plan
That you have done sufficient work on the project
An ability to write up work with sufficient rigour
An ability to do experimental and computational work of sufficient level for the project
An ability to do more theoretical or mathematical work of sufficient level for the project
An understanding and knowledge of the background work in the field
A sufficiently organised, systematic and joined up approach to research
Progress in the project, the research and the understanding, with some novel research contribution
That advice or collaboration from others is sought and considered
An ability to present your work to an audience.

Where an element of this is missing, this will be discussed in the annual review with a plan made for its achievement. These annual review criteria are not explicitly considered in the project marking. The project marking focuses on scholarship. The research content is the subject of the PhD research project.

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IDB: Introduction to Databases

aburford — Fri, 29 Aug 2025 12:32:48 +0000

IDB: Introduction to Databases aburford Fri, 29/08/2025 - 13:32

Welcome to Introduction to Databases (IDB).

Please note that the schedule below is tentative and may be subject to change during the course.

Week	Topic
Week 1	Overview \| Basic SQL
Week 2	Relational Algebra \| Predicate Logic
Week 3	Predicate Logic \| Relational Calculus
Week 4	Equivalence of Relational Calculus and Algebra
Week 5	Multisets, aggregation, grouping
Week 6	Subqueries and other bits of SQL
Week 7	Database constraints and entailment
Week 8	Normal forms \| SQL constraints and triggers
Week 9	Transactions management
Week 10	SQL in programming languages \| The NULL value
Week 11	Exercises in preparation for the exam

Advanced Topics

One or more of the following may be covered if time allows:

Data Storage and Indexing
Query Processing
Incomplete Information
Deductive Databases.

Files

Equivalence between RA and RC

Deductive Databases (Datalog)

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ILP: Informatics Large Practical

aburford — Tue, 12 Aug 2025 13:57:56 +0000

ILP: Informatics Large Practical aburford Tue, 12/08/2025 - 14:57

Welcome to the home page of the ILP course.

ILP is intended as a purely practical contemporary course which equips you with the knowledge to create a large individual software development project. Our main goal is to provide you with the necessary skills to thrive in the modern cooporate world with a lot of background information and insights.

What will you learn:

How to write a REST-service
How to use docker and docker images to run everything
How to consume and process JSON data
How to interact with other services using http-conversations
How to plan and execute a larger project
How all this fits into the contemporary Gen-AI based world of software engineering

The programming language we use is Java (that is mandatory) and the framework Spring, yet everything can be applied to any other modern language as well.

The course contents are defined in the corresponding document below - please study them as they contain quite some background information and details which might be useful.

The schedule of the lectures, tutorials, etc can be found here.

Course structure

We will be delivering the course via lectures, some tutorials in small groups mainly to give you the necessary skills for your submissions, demonstrations/drop-in lab sessions, and a chat forum (piazza).

Aside that there is a - absolutely non-compulsary - part called BOF which allows us to give you additional context / content related to software engineering, contemporary praxis, etc. It is intended as a additional information source, but not part of the required knowledge to pass the course and it will not be recorded.

The team

ILP will be delivered by 2 lecturers:

Dr Cristina Alexandru
Dr Michael Glienecke (course organizer)

In addition to that there is Cameron Wheeler (PhD student) who acts as the TA and will support you on piazza, Q&A, etc.

The assessments

This year we will try something unconventional to spice up the course and (hoipefully) make it much more fun for you.

Details are about to follow at a later time, yet the main intention is to make this so much more tangible and joyful for you!

To cover some factual knowledge, there will be an oral examination in person where you will have a pick of 2-3 questions you will have to elaborate. Nothing too fancy - so, if you followed the class and understand the principles you should have no problem at all passing.

Saing this, to get a real good mark will require a considerable amount of work and contribution, within reasonable limits.

For those of you who face special challenges, please let us discuss how we can mitigate. We do have many ideas, but as this is a very personal issue, many will prefer more custom solutions.

Lecture recordings

So, please enjoy and have a lot of fun

Files

ilp2025.pdf

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CSS: Understanding Society with Big Data: Computational Social Science

mcorey — Thu, 24 Jul 2025 10:51:49 +0000

CSS: Understanding Society with Big Data: Computational Social Science mcorey Thu, 24/07/2025 - 11:51

Welcome to Understanding Society with Big Data: Computational Social Science

Learning Outcomes

On completion of this course, you will be able to:

formulate research questions about social phenomena and human behaviour
identify the appropriate big data and computational methods to answer these questions, taking into account ethical challenges and potential biases.
apply basic data analysis and descriptive statistics on big (e.g. textual, network) data
explain findings and their implications and limitations to a wide audience.
collaborate responsibly and effectively in an interdisciplinary setting

Course Outline

The principal aim of the course is to teach how to conduct a computational social science project by introducing all the necessary components: formulating questions, collecting data, designing experiments, employing methods of artificial intelligence and text analysis and considerations of bias, fairness and ethical implications.

Indicative list of topics:

Fundamentals of Computational Social Science
Quantitative & Qualitative Concepts
Data & Methods & Exploration
Machine Learning
Ethics & Fairness
Network Analysis
Text Analysis
Large Language Models (LLMs)
Data Sources & Collection
Story-building for Data Science

The course will be taught through a mix of lectures, where new content will be introduced, and labs and tutorials where you will get hands-on experience putting these concepts into practice.

Lecturers

School of Informatics

Tuğrulcan Elmas
Björn Ross
Walid Magdy

School of Political & Social Science

Clare Llewellyn
Tod Van Gunten
Peaks Krafft

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NLP-CS: Case Studies in Responsible Natural Language Processing

mcorey — Tue, 15 Jul 2025 11:12:08 +0000

NLP-CS: Case Studies in Responsible Natural Language Processing mcorey Tue, 15/07/2025 - 12:12

Welcome to Case Studies in Responsible Natural Language Processing

Learning Outcomes

On completion of this course, the student will be able to:

critically evaluate the literature on legal, social, and ethical aspect of NLP
working with partners, analyse legal, social, and ethical implications of deploying NLP technology across various application domains
design potential solutions to legal, social and ethical problems, combining engineering and design thinking

Course Outline

This course will enable students to practice responsible research and innovation in action. They will reflect on their learning in areas of legal, social, and ethical aspects of AI and NLP and put this learning into practice by working on case studies on responsible NLP. The course will have an interdisciplinary outlook, and the case studies will be provided by the industry partners of the CDT. Example topics include:

fairness and bias
social issues of model deployment
impact of AI and NLP technology on the workplace
data privacy, copyright, and other legal implications of NLP
translation of ethical and moral values to technical systems
political influence and manipulation with the help of AI and NLP
generative AI and the creative industries

The students will engage in two main activities: (1) student-lead seminars in which students present topics in responsible NLP based on the courses they've taken in year 1; (2) partner presentations introducing case studies on responsible NLP; each student will select one of these case studies and work with the partner on an analysis of that case study, drawing on their knowledge from the first part of the course.

This is a course-work only course; the students will be assessed on their seminar presentations and on a report of their case study analysis.

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FINET: Financial Networks

aburford — Tue, 08 Jul 2025 10:40:29 +0000

FINET: Financial Networks aburford Tue, 08/07/2025 - 11:40

Welcome to Financial Networks

Learning Outcomes

On completion of this course, the student will be able to:

critically analyse and explain complex financial systems based on empirical observations.
select and justify appropriate network analytics techniques for specific financial network tasks.
implement network analytics methods to uncover insights into financial behaviours and dynamics.
evaluate how network structures influence financial performance and risk, interpreting the implications for various financial scenarios.
present highly interdisciplinary work in an understandable and comprehensive manner to people with different backgrounds.

Course Outline

What if you could predict how a financial crisis unfolds before it even begins? In this course, you will dive into the networks that shape the financial world, discovering how connections between institutions, markets, and people drive it. Rather than focusing on individual entities, you will explore the web of relationships influencing everything from financial contagions to the rise and fall of cryptocurrencies, and spending behaviour. Through practical examples, you will build a toolkit for analysing complex financial systems. Whether you are interested in banking, digital finance, or economic policy, this course offers fresh insights into finance.

Schedule and information

LECTURES

Tuesdays 15:10-16:00 (Dugald Stewart Building - 3.10)
Thursdays 15:10-16:00 (40 George Square Lower Teaching Hub - LG.11)
Fridays 15:10-16:00 (40 George Square Lower Teaching Hub - LG.09)

TUTORIALS

Wednesdays (Lister Learning and Teaching Centre - 2.14 - Teaching Studio)
There are two tutorials, one at 14:10-15:00, the other at 15:10-16:00.

Please check which group you belong to. If for any reason you cannot attend a tutorial in your group, try to attend the other and let me or the TA know, so we can still record your attendance.

Tutorials have been moved to accommodate the clashes some of the students let me know about. If you do anticipate having a clash with your tutorial group time, please make a request to change your tutorial group.

There will be no lectures and tutorial on week 11 and another week yet to determine, to let you focus on the coursework and the revision of the material in preparation of the exam.

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ATNLP: Advanced Topics in Natural Language Processing

mcorey — Fri, 27 Jun 2025 15:26:00 +0000

ATNLP: Advanced Topics in Natural Language Processing mcorey Fri, 27/06/2025 - 16:26

[Quick Navigation during the semester: Course materials / Tutorials / Labs / Assignment / Piazza Forum / Time table ]

Welcome

The Advanced Topics in Natural Language Processing is a new 20-credit course that covers a set of topics from the modern era of NLP, with a focus on large language models (LLMs). Please see more details in the "Course Materials" page. The structure of the course is such that each week two lectures are given about the core material, and one lecture is given by an additional staff member in NLP who will discuss their work.

Disclaimer: The choice of topics we cover in this class may seem eclectic at first, and in many ways, it is. In recent years, the field of natural language processing has spawned numerous subareas that have become fertile ground for research and work, which can sometimes be considered fields in their own right. This expansion is especially noticeable, as NLP is now also often identified more generally with artificial intelligence. To put it simply, NLP is now a vast field.

In our choice of "advanced topics", we tried to (a) keep the topics appropriate for all, given the diversity of student backgrounds in the course; (b) keep the topics as "foundational", "representative" and "durable" as possible (to predict), so they can indeed serve better as a scaffold for students who are interested in working in the area in the future; (c) keep the topics at a level that indeed requires guidance and conceptual understanding that are better delivered in lectures rather than self-learning (though self-learning is *highly* encouraged throughout the course).

With that being said, we are open to discussing any other topics during office hours with students, or connecting them to lectures through questions or enquiries during the lectures.

Lecturers: Alexandra Birch, Shay Cohen, and Alessandro Suglia

Note that ANLP (or equivalent) is a prerequisite to take this course.

Where do I find things?

On this site, you will find publicly available information, including all course materials other than the assessed coursework.

On the course Learn site (linked at the top of this page) you will find information that requires a university login:

all information about assessment, including
- instructions and downloads
- due dates and late policy
- links to submission inboxes
a link to the course discussion forum

Readings

Some of the readings for ATNLP derive from the latest edition of the Jurafsky and Martin Speech and Language processing textbook:

Third edition, draft as of August 2025 (denoted JM3). We will always refer to chapters in this complete PDF. You can download individual chapters from the textbook website, but these can change without warning.

Past students have requested that we prioritize essential readings, so we make clear what are essential chapters/papers to read and we also link to important optional readings.

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