# Integrated Computer Science

**Source**: https://teaching.scss.tcd.ie/integrated-computer-science/ics-year-5/
**Parent**: https://teaching.scss.tcd.ie/module/stu22005-applied-probability-ii/

## Year 5 (Master in Computer Science)

All Year 5 students take a Research Methods module and undertake a significant dissertation project (30 credits). They also select five elective modules from the list below counting for 25 credits some of which are run in Michaelmas term and some in Hilary term.

[Integrated Computer Science Handbook 2025-2026](https://teaching.scss.tcd.ie/wp-content/uploads/sites/10/2025/09/Integrated-Computer-Science-Handbook-2025-2026-5.pdf)

[Year 5 Option Form 2025/26](https://forms.office.com/Pages/ResponsePage.aspx?id=jb6V1Qaz9EWAZJ5bgvvlK56NmYMGYYBAi610rWWqW9RUM0ZMREdCRDZZSkpLMEpSN0VJQzhESVFMNi4u)

[Quick Links](https://teaching.scss.tcd.ie/general-information/quick-links/)

## Core Modules

Module CodeCS7092Module NameDissertationECTS Weighting [1]30 ECTSSemester TaughtSemester 2Module Coordinator/s  Assigned Supervisor Module Learning Outcomes On successful completion of the project, the students will be able to: Module Content This research…

(Semester 1, 5 ECTS) Locate, obtain and critique relevant knowledge and evidence to support innovation and research

## Elective Modules

Students in Year 5 choose five of the options below. The form to choose your options can be found at the following link:

[Year 5 Option Form 2025/26](https://forms.office.com/Pages/ResponsePage.aspx?id=jb6V1Qaz9EWAZJ5bgvvlK56NmYMGYYBAi610rWWqW9RUM0ZMREdCRDZZSkpLMEpSN0VJQzhESVFMNi4u)

(Semester 1, 5 ECTS) Understand what machine learning is and how it works.

(Semester 1 & 2, 10 ECTS) To understand the theory and be able to apply the following techniques\
to a set of data.

(Semester 2, 5 ECTS) The aims of this module are to give the student skills to model, analyse and solve optimisation problems that arise in data analytics and modern computing and communication systems.

(Semester 2, 5 ECTS) This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.

(Semester 1, ECTS 5) This module aims to equip the student with the knowledge and tools to visualise data in ways that give insight and understanding.

(Semester 1, 5 ECTS) Image processing, feature detection and matching, image registration, recognition\
and segmentation – Motion flow and object tracking in video – Mathematics for\
computer vision.

(Semester 1, 5 ECTS) Wave equation and its solution; Maxwell´s equations; Fourier transform and analysis; vibration; mass-spring-damper systems; numerical methods; simulation software.

(Semester 2, 5 ECTS) This module deals with programming for GPU pipeline architectures e.g. geometry,\
rasterisation, texturing, fragment / pixel and vertex shaders.

(Semester 2, 5 ECTS) The aim of this module is to provide students with a deep understanding of the theory and techniques behind real time animation.

(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.

(Semester 1, 5 ECTS) The module is designed to explore the management, delivery and inter-operability of knowledge, information and data through knowledge and data engineering.

(Semester 2, 5 ECTS) Appreciate the scope, applications and limitations of artificial intelligence;

(Semester 1, 5 ECTS) Explain the process of content indexing in information retrieval including stop word removal, conflation (stemming, string-comparison), and the language dependency of these methods.

(Semester 2, 5 ECTS) Grasp the scope and limitations of finite state methods in text analysis.

(Semester 2, 5 ECTS) User modelling, including Task modelling\
User preferences

(Semester 1, 5 ECTS) This module aims to provide a theoretical and practical understanding of modern scalable systems and architectures, from billions of highly distributed Internet of Things devices, through to present and future concepts, such as Quantum and Nanotech systems.

(Semester 2, 5 ECTS) In this module, students will explore the prevailing vision for an Internet of Things in\
a practical, pragmatic manner.

(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of\
modern and next generation networking and systems concepts, principles, practices\
and technologies. Contemporary and emerging wired and wireless network systems\
are targeted.

(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of urban\
computing and associated cyber-physical concepts, principles, challenges and\
solutions.

(Semester 2, 5 ECTS) The objectives of this module are: to develop an in-depth understanding of risk, data\
privacy, threats and risks of security breaches, an awareness of computer security\
(cryptographic) and protocol techniques, and an ability to make appropriate\
decisions about securing data.

(Semester 2, 5 ECTS) This course takes a critical look at some of the architectural issues involved in, and paradigms available for, the construction of large-scale distributed systems such as the infrastructures supporting Google’s search engine or Amazon’s online sales platform. In particular, the course considers how to develop systems that must make trade-offs between performance, consistency, reliability, and availability.

(Semester 2, 5 ECTS) This module focuses on practical application of machine learning techniques to radio and optical transmission networks. It will start with an overview of the machine learning techniques that are applicable to some specific problems in the networking domain and then provide deeper insight into those that will be used in the lab to address the specific use cases described below

(Semester 2, 5 ECTS)\
This course covers fundamentals and state-of-the-art in augmented reality, as well\
as related areas of 3D computer vision and graphics.

(Semester 2, 5 ECTS) Explain how high tech venture creation operates, with an emphasis on the processes developed by the Silicon Valley venture community over the past 20 years