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MSc INF Modules

MSc Informatics Programme Structure for the Dissertation Route

Students study 4 core taught modules and 2 modules of 20 credits from the list of electives and complete a 60 credit research-based dissertation. The award of MSc IT is approved following the successful completion of 180 credits. The following is a summary of modules per stream:

 

Module Code

Module Title

Credits

Core: Complete all of the following modules

INF501

Informatics Research Methods

20

INF502

Knowledge Representation & Reasoning

20

INF503

Introduction to Computational Linguistics

20

INF504

Data Mining and Exploration

20

Electives:  (Student will be required to take two out of the six modules)

INF505

Knowledge Engineering (pre-requisite INF502, Knowledge Representation & Reasoning)

20

INF506

Knowledge Management

20

INF513*

Machine Learning (pre-requisite INF504, Data Mining & Exploration)

20

INF508

IT Project Management

20

INF509

E-commerce

20

INF510

IT Entrepreneurship

20

INF511

Software Systems Design: Practical Object-Oriented Analysis and Design with UML

20

INF512

Systems Requirements Engineering

20

INF514

Management Information Systems

20

Independent Research

RES506

Dissertation

60

Total Credits

180

       

*INF507 Learning from Data module name has been revised to Machine Learning. 

 

Recommended Study Plans

 

TERM 1

INF501 Informatics Research Methods (Core)

INF503 Introduction to Computational Linguistics (Core)

INF507 Learning from Data

INF510 IT Entrepreneurship

 

TERM 2

INF502 Knowledge Representation & Reasoning (Core)

INF504 Data Mining and Exploration (Core)

 

TERM 3

INF506 Knowledge Management

INF508 IT Project Management

INF509 E-commerce

INF513 Machine Learning

 

MSc Informatics Programme Structure for the Project-Based Route

Students study 8 taught modules (6 core, 2 modules from Electives) and a project. Essentially the 60 credit dissertation in the existing structure is replaced with 2 modules of 20 credits each and a project of 20 credits.

 

Module Code

Module Title

Credits

Core: Complete all of the following modules

INF501

Informatics Research Methods

20

INF502

Knowledge Representation & Reasoning

20

INF503

Introduction to Computational Linguistics

20

INF504

Data Mining and Exploration

20

INF508

IT Project Management

20

INF506

Knowledge Management

20

INF520

MSc Project*

20

Electives SET 1:  (Student will be required to take two out of these electives)

INF513*

Machine Learning (pre-requisite INF504, Data Mining & Exploration)

20

INF505

Knowledge Engineering (pre-requisite INF502, Knowledge Representation & Reasoning)

20

INF509

E-commerce

20

INF510

IT Entrepreneurship

20

INF511

Software Systems Design: Practical Object-Oriented Analysis and Design with UML

 

INF512

Systems Requirements Engineering

 

INF514

Management Information Systems

20

Total Credits

180

*INF507 Learning from Data module name has been revised to Machine Learning. 

 

Recommended Study Plans

 

TERM 1

INF501 Informatics Research Methods (Core)

INF503 Introduction to Computational Linguistics (Core)

INF507 Learning from Data

INF510 IT Entrepreneurship

 

TERM 2

INF502 Knowledge Representation & Reasoning (Core)

INF504 Data Mining and Exploration (Core)

 

TERM 3

INF506 Knowledge Management (Core)

INF508 IT Project Management (Core)

INF513 Machine Learning

INF509 E-commerce

 

Postgraduate Diploma

Students may initially register for PG Dip or MSc at any time. Students registered for the PG Dip may apply to transfer to the MSc at any time. Exit awards (PG Dip) may be recommended by the Board of Examiners to students who do not complete their MSc for which they are registered but were able to complete 120 Credits.

 

Modules

Informatics Research Methods

The aim of this module is to teach the methodologies of and the skills for conducting research in Informatics. It will focus on three main parts: (1) analytical methods, (2) empirical methods, (3) writing and evaluating research. The module will cover: the nature of Informatics and Informatics research; criteria for assessing Informatics research; different methodologies for Informatics research and how to combine them; analytical proof; algorithm and complexity analysis; the design of experiments and evaluations; practical advice on conducting research and numerous research skills including: reading, reviewing, presenting, writing, design, etc.

 

Knowledge Representation & Reasoning

This module provides the basis for the understanding and use of Knowledge Representation and Reasoning techniques in AI systems in general, and knowledge-based systems in particular. The module covers notions of representation and the relationship between representation and that which is represented, along with issues of the resources required to manipulate such representations. The focus is on different logic-based representation languages and proof search using logical calculi, but other approaches are also discussed.

 

Introduction to Computational Linguistics

This is an introductory course that presumes no prior familiarity with Computational Linguistics.  This course provides an introduction to the basic theory and practice of computational approaches to natural language processing. The module cover the following topic:  introduction to programming in Python & NLTK, tokenization, part-of-speech tagging, context-free grammars for natural language, evaluating a natural language processing system, parsing techniques, information extraction, Arabic language processing. The course also provides an introductory insight into the state of current research in Computational Linguistics.

 

Data Mining & Exploration

Data mining is about analyzing, interpreting, visualizing and exploiting the data that is captured scientific and commercial environments. The course will also feature paper presentations and a each student will undertake a mini-project on a real-world dataset.

 

IT Project Management

This module is about IT project management activities. Covered topics include software systems engineering, project planning and management, quality assurance, and strategic planning. The student will learn to manage software as a distinct project, use specifications and descriptions, make use of structured techniques, complete reviews and audits, confirm product development with planned verification, and validation and testing. Students will work with essential tools and methodologies for managing an effective IT project, including software for version control, and project management.

 

Knowledge Management

The aim of this module is to teach the principles and technologies of knowledge management. A case study approach, as and where appropriate, will be adopted in introducing the course contents. The module covers the fundamental concepts in the study of knowledge and its creation, representation, dissemination, use and re-use, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, knowledge acquisition, and how to apply a knowledge management system using one of the knowledge-based system tools.

 

Knowledge Engineering

This module introduces a variety of methodologies important to the development of modern knowledge-based systems (KBSs) and their applications, especially pertaining to the Semantic Web. The module covers topics regarding different processes within a KBS lifecycle, ranging from knowledge capture and analysis, systems design and implementation, to knowledge maintenance and system evaluation. Students will learn about the latest applications of KBS in building intelligence into Web applications, and will build a knowledge-based Web application.

 

Machine Learning

Machine learning is about making computers learn, rather than simply programming them to do tasks. The course will discuss supervised learning (which is concerned with learning to predict an output, from given inputs), reinforcement learning (which is concerned about learning from interacting with an environment), unsupervised learning, where we wish to discover the structure in a set of patterns; there is no output "teacher signal". We will compare and contrast different learning algorithms, and unlike Data Mining Exploration module where the focus was on the applying algorithms to large real-world data sets, in this course we will get to the technical and mathematical details of the studied algorithms.

 

E-Commerce

This module is about topics related to creating a business on the web, with particular focus on e-commerce. Students will study the IT issues raised by electronic business and commerce. Techniques and technologies available for designing and implementing e-business and e-commerce applications will be surveyed. Students will have first-hand experience with Web-based tools and services to help design e-Business solutions.

 

IT Entrepreneurship

This module provides the students with scientific methodologies for identifying opportunities in the IT space. Students will learn how to create an effective business plan, acquiring funding, establishing a company from scratch and managing in an environment of high growth, high uncertainty and rapid change.

The module will include case studies of successful and failed IT entrepreneurial companies and will draw upon the angel investing, venture capital and entrepreneurial communities from guest speakers.

 

Software Systems Design: Practical Object-Oriented Analysis and Design with UML

This course is designed to give students knowledge of the principles of object orientation and extensive practice in the application of these principles using the Unified Process (UP) and Unified Modelling Language (UML). It guides the students through the process of UML system modelling approach and from requirements analysis to implementation. The course is very practically oriented and follows the Unified Process so that the students learn how UML is applied in a real software systems engineering project. The course will also give students knowledge of Model Driven Architecture (MDA).  MDA is the future of UML and unifies every step of software systems development and integration from business modeling, through architectural and application modeling, to development, deployment, maintenance, and system evolution.  The goal of MDA is to move the development of software to a higher level of abstraction through the extensive use of UML models. These models provide the basis for automatic code generation by MDA enabled CASE tools.

 

Systems Requirements Engineering

The general aims of this course is to make students understand the ever-increasing importance of requirements in the wider systems engineering process, and to improve systems engineering processes by making them more requirements-oriented. The course describes the role of requirements in the construction and continued maintenance of large, complex and evolving software-intensive systems. It introduces the important concepts and activities in systems requirements engineering, explains how they can knit together to form a through-life requirements engineering process, and demonstrates how such an end-to-end process can be defined and used in practice. The course provides a broad overview of the notations, techniques, methods and tools that can be used to support the various requirements engineering activities, and complements this with the opportunity to gain experience in a selection of these. The course seeks to illustrate the wider applicability of requirements engineering to everyday projects, the breath of skills required and the many contributing disciplines.

 

Management Information Systems

This module is about determining the information system needs for designing and implementing information systems that support these needs. Management information systems integrate, for purposes of information requirements, the accounting, financial, and operations management functions of an organization. This course will examine the various levels and types of software and information systems required by an organization to integrate these functions.

 

Dissertation

Having successfully completed the six modules in the taught stage of the programme, students who wish to proceed to the master’s degree take the dissertation stage. This final project is intended to give students an opportunity to focus on an aspect of the taught subject matter and investigate it in more detail. This will help them consolidate their capacity for independent study, and to learn some of the techniques needed to conduct research and develop knowledge in the subject area of the programme of study.

This is a research project. The only piece of work to be submitted for examination is a dissertation, and this is a written report on the research. There are thus two aspects to consider: the research and the writing. Both are governed by implicit rules common to the discipline of formal research; part of the students’ training is to become familiar with these rules.

 

MSc Research project

In this module the student will undertake a short research project. This project could be an extension of one or more projects submitted in previous modules. In this module the student will reflect on all his/her research activities in the previous modules, will undertake critical review of previous outcomes in order to prepare a proposal for new research project. The student will focus on applying the knowledge learnt in several modules to analyse, revise, improve and assess a relevant topic. This could include topics on Artificial Intelligence, Intelligent Systems, Knowledge Management, Learning from Data, Software Engineering, IT & management, or any other relevant IT topic as long as it is approved by the module tutor. The student will produce a research report, including an executive summary, reflective analysis of previous works, and details of the project outcome.

The entry requirements for the MSc in Informatics programme.

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