Cognitive Science 1: The Ghost in the Machine

15 credits
Autumn teaching, Year 1

What is it to be an intelligent embodied person? One common view is that mind and body belong to two different metaphysical realms, fused together in us as if we were a combined ‘ghost in a machine’ (to use the famous words of the philosopher, Gilbert Ryle, spoken 50 years ago). Few people believe such a view can work, but what should replace it? We look at a number of different ‘materialist’ theories, concentrating on variants of the computer model, and on neuro-physiologically based accounts of mind. In doing so, we examine some of the basic issues underlying cognitive science as an interdisciplinary study of the mind, taking in topics from psychology, neuroscience, linguistics, computing, artificial intelligence, robotics, evolutionary theory, biology and philosophy.

Data Structures & Algorithms

15 credits
Spring teaching, Year 1

This module provides an introduction to data structures and algorithms for computer scientists. The module introduces a number of fundamental data structures, including arrays linked lists, stacks, queues, trees, hash tables and graphs. These are presented both abstractly, via the notion Abstract Data Types, and concretely in terms of their implementation in an object-oriented framework. The data structures are discussed and analysed in terms of efficiency of the basic operations they support and their application to program design problems. Consideration is given to important, fundamental algorithms for searching and sorting data.

Further Programming

15 credits
Spring teaching, Year 1

This module follows on from "Introduction to Programming" and provides an introduction to more advanced programming concepts and techniques. This module covers Java programming, including the use of subclasses and library classes to create well-organised programs, the choice and implementation of appropriate algorithms and data structures (e.g. arrays, lists, trees, graphs, depth- and breadth-first search, the minimax and A* algorithms), and the construction of graphical user interfaces for Java programs.

Introduction to Computer Systems

15 credits
Spring teaching, Year 1

Topics covered on this module include: the key elements of a computer; how information is stored - from transistors to files; how information is processed - from logic circuits to programmes; how information is transferred - from buses to the internet; computers and the physical world - peripheral devices and embedded computers; operating systems and virtual machines; and the history and future of computing.

Introduction to Programming

15 credits
Autumn teaching, Year 1

The module introduces you to a collection of basic programming concepts and techniques, including designing, testing, debugging and documenting programmes.  

For both absolute beginners and those with prior computing experience, the module introduces the programming language Java, a language used for other components of undergraduate modules. Java will be the primary language used for programming assignments in nearly all first year modules taught by the department of Informatics.  

You do not need previous experience of programming to take this module, but you will need basic knowledge of NT/Windows2000/XP.

Mathematical Concepts

15 credits
Autumn teaching, Year 1

A refresher mathematics module covering sets and functions, vectors and matrices, proof by induction and simple numerical integration.

Professional Skills

15 credits
Spring teaching, Year 1

This module will cover important professional skills in 4 categories: Technical communication, technical and academic writing, professional conduct, and IT law.
Topics include:
Technical communication skills (2 lectures)
1. Giving effective oral presentations
2. Graphical aids for oral presentations
Writing skills (12 lectures)
1. Report writing
2. Reviewing
3. Correct attribution of credit and referencing
Professional conduct (6 lectures)
1. Codes of professional conduct
2. Computers and Society, including the workplace and education
3. Ethical implications of the internet, artificial intelligence, virtual reality, and emerging new technologies
IT law (4 lectures)
1. Digital evidence: Information retrieval, retention and protection
2. Privacy and data protection
3. Contract law and employment law for IT
4. Intellectual property in the IT sector

Programming Concepts

15 credits
Autumn teaching, Year 1

This module introduces algorithmic problem solving.  It will answer the following questions: what is a problem specification, an algorithm, and a computation?  What are their properties?  How does one develop an algorithm? How can one rigorously argue that an algorithm computes correct solutions to a given problem? How can one measure the efficiency of an algorithm and the complexity of a problem?

For the sake of writing algorithms, a simple algorithmic language (pseudo code) is used. The focus is on algorithmic thinking, not coding. Basic data structures will be used to provide some elementary examples. Searching, sorting and other simple (and intuitive) algorithms can then be specified and developed. Principles like divide­-and­-conquer will be applied and explained.  

Two important properties of algorithms are correctness and complexity. Algorithms should only compute correct solutions of a problem, and to establish correctness, you will consider relevant (propositional and predicate) logic, focusing on logical reasoning principles rather than logical calculi. Finally, you will discuss the concept of the time complexity of an algorithm and asymptotic complexity classes.

The exercise classes and coursework are based on a series of examples. The algorithms developed in this module should be implemented in Java concurrently or at a later stage in the further programming module.

Acquired Intelligence & Adaptive Behaviour

15 credits
Spring teaching, Year 2

Using AIAB as a paradigm (similar to 'Artificial Life' or 'New Wave AI') you will consider: evolutionary (genetic) algorithms; artificial neural networks; situated, embodied approaches to the study of intelligence and robotics; and the study and exploitation of emergence in complex systems (such as cellular automata and agent-based models).

AIAB will be considered scientifically as well as in terms of its practical uses.

Computer Vision

15 credits
Spring teaching, Year 2

The module introduces the field of computer vision and its relation to research on natural vision systems. Topics include: the functions of vision; finding image structure; the determination and representation of 3-D surface shape; visual motion; object recognition; active vision. The module emphasises practical techniques and you will be introduced to a suitable software package.

Databases

15 credits
Autumn teaching, Year 2

This module provides an introduction to the concepts of database software, database design, management and programming.  This includes conceptual database design (using the entity-relationship approach), logical database design and physical database design. The module focuses on the relational data model only. SQL is presented as data manipulation language to implement the physical design. It is explained how SQL can be used to create and manipulate relational databases. Database normalisation is motivated and presented (in a restricted form using primary keys only). Security via permission rights and indexes fortuning database queries are briefly addressed. Database programming is explained and demonstrated using Java Database Connectivity (JDBC) libraries. The exercise classes and coursework are based on a series of examples that are used to elucidate the theoretical principles. You will acquire practical experience by implementing these examples in a database management system.

Machine Learning

15 credits
Spring teaching, Year 2

This module provides you with an introduction to the important field of machine learning that aims to present a diverse range of concepts and techniques without losing sight of the unifying principles of the field.

Machine learning approaches will be considered in terms of three key ingredients: tasks, models and features. You will be introduced to binary classification and related tasks and issues in the evaluation of classifier performance. The concept of learnability will be considered.

A number of machine learning approaches will be introduced, including: linear regression, the perceptron classifier, decision tree models and rule induction, instance-based learning, the naïve bayes classifier and the k-means clustering algorithm.

Throughout the module, an applied, example-based approach will be adopted in presenting the material. Where mathematics is needed to understand a particular technique or concept, it will always be reviewed in advance.

Natural Language Engineering

15 credits
Autumn teaching, Year 2

Natural Language Engineering introduces techniques and concepts involved in analysing of text by machine, with particular emphases on various practical applications that this technology drives.

Topics covered on the module will include both a variety of core, generic text processing models (e.g. , segmentation, stemming, part-of-speech tagging, named entity recognition, phrasal chunking and dependency parsing) as well as problems and application areas (e.g. document classification, information retrieval and information extraction).

We will be making extensive use of the Natural Language Toolkit which is a collection of natural language processing tools written in the
Python programming language.

Program Analysis

15 credits
Autumn teaching, Year 2

Part 1: Foundations

The first part of the module introduces the idea of the asymptotic analysis of algorithms, and in particular we will consider the following: specifying a problem; the notion of an algorithm and what it means for an algorithm to solve a problem; the upper, lower and tight asymptotic bounds associated with an algorithm; the best-, worst- and expected-case analysis of an algorithm; the lower bound for a problem.

In the remainder of Part 1 we consider a number of important data structures, with particular emphasis on priority queues and the generic graph data structure. Several basic graph algorithms will be considered, in particular: depth-first search of graphs; breadth-first search of graphs; and topological sorting of directed acyclic graphs.

Part 2: Generic Design Paradigms

In Part 2 we will consider four of the most important methods used as the basis for algorithm design: greedy methods; divide and conquer approaches; dynamic programming; and network flow.

In considering these generic design paradigms we will look at a number of well-known problems, including: interval scheduling; single source shortest path; minimum spanning tree; Huffman codes construction; weighted interval scheduling; subset sum; sequence alignment; network flow; and bipartite matching.

Abnormal and Clinical Psychology

15 credits
Spring teaching, Year 2

This module introduces you to the main diagnostic categories of psychological disorders, the major theories of causation and approaches to treatment, and encourages you to appreciate the links between the theory and the treatment of those disorders.

Brain and Behaviour

15 credits
Autumn teaching, Year 2

This module provides an introduction to brain mechanisms and behaviour. Topics covered will normally include: functional neuroanatomy of the human brain; ionic mechanisms underlying the nerve action potential; synapses and neurotransmission; neuropharmacology of commonly used anxiolytic drugs; neural mechanisms in emotion and motor behaviour; and neural mechanisms underlying plasiticity and learning.

Compilers and Computer Architecture

15 credits
Autumn teaching, Year 2

Topics on this module include: low­-level versus high-­level languages; an introduction to language implementation techniques, compilers and interpreters, grammars and parsing; hardware implications, instruction set design and implementation; lexical analysis; ­ the relevance of finite­state automata and regular grammars; implementation techniques; problems for particular languages, syntax analysis ­ overview of grammars and parsing techniques; top­-down and bottom­-up parsing; predictive parsing, shift-­reduce parsing; implementing hand­-coded top­-down predictive parsers; semantic analysis and code generation ­ from trees and from flat intermediate codes; symbol tables; type checking; handling of specific high­-level language constructs; runtime storage allocation and scoping; instruction set consequences; hardware aspects of performance enhancement ­ caches, pipelining and parallelism; recent developments in processor design; code optimisation and an introduction to flow analysis.  

Neural Circuits

15 credits
Spring teaching, Year 2

This module will teach you about neural mechanisms generating animal behaviour. The level of analysis emphasises types of behaviour that can be understood in terms of underlying neural circuits or specific structures with well­ known neural architectures within the brain.

Topics covered include:

• organisation and modulation of central pattern generator (CPG) circuits
• advanced techniques for monitoring and manipulating neural circuits
• modelling of neural circuits
• sensory and motor functions of spinal cord circuits
• brain circuits underlying motor control
• circuits underlying non-associative and associative learning
• addiction and learning circuits
• defects in circuits
• development of neural circuits

Philosophical Foundations of Cognitive Science

15 credits
Autumn teaching, Year 2

This module examines various materialist conceptions of the mind, especially the functionalist vision of the mind as a kind of computer program running in the brain. Symbolic and connectionist versions of this view are described and compared. The complex issues surrounding the scientific explanation of consciousness and experience are discussed.

Philosophy and Science of Consciousness

15 credits
Spring teaching, Year 2

The module examines the problems and prospects for a science of consciousness. Topics include: defining consciousness, the Hard Problem, the Knowledge Argument against physicalism, qualia, theories of the self, the neuroscience of consciousness, attention and volition, machine consciousness, the evolution and function of consciousness, the Grand Illusion theory.

Principles of Neuroscience

15 credits
Autumn teaching, Year 2

In the first half of this module we will study in detail how plants sense their environment (plant growth regulators), take up nutrients (ion transport and membrane properties) and photosynthesise (carbohydrate synthesis, phloem translocation and sink tissue metabolism). We will then focus on the molecular biology of plants, and topics covered will include compartmentation of plant DNA, plant gene expression, and the plant genome. This will lead on to lectures on plant genetic manipulation and the application of such technologies.

Software Engineering

15 credits
Spring teaching, Year 2

This module studies large-scale software production. Emphasis is placed on the whole life-cycle of a software product: requirement analysis, software architecture and design, implementation, quality assurance and maintenance activities. The module also investigates social issues in software engineering such as team-structures and conflict management. Other issues covered include agile software engineering methods, testing, test-driven development, coding practice and standards, design and code reviews, and version control.

Coursework will be team-based and involve the production of a significant software deliverable such as an interactive gaming application.

Computer Science and Artificial Intelligence Project

45 credits
Autumn & spring teaching, Year 3

Prerequisites: Projects should be related to modules taught in the first two years. Independent study resulting in a dissertation. Tutorials are to support this activity.

This module will give you the opportunity to complete an extensive piece of research, design or implementation work under the supervision of a member of faculty. You will be able to chose from a range of project topics or offer a project of your own. All topics will require the application of skills and knowledge gained through previous modules of study and will involve you in the design and implementation of a technological solution to a Computing and Artificial Intelligence related problem (using programming, modelling, simulation tools as appropriate). Some project topics will be available in collaboration with commerce and industry and will enable you to experience the methods and approaches of non-academic institutions. There will be no formal lectures to attend. The teaching methods used will simply be weekly individual/small group meetings to discuss progress.

The final year Computing and Artificial Intelligence projects should be viewed as the culmination of the degree - it gives you a chance to demonstrate all you have learned. It will be the most demanding part of the undergraduate degree. It is very different from most other modules. Although you will be supervised, you are on your own to a large extent. The onus is on you to define the problem boundaries, to investigate possible solutions, and to present the results verbally, in writing and (possibly) to demonstrate in action.

The results of the project will be submitted in report form to be examined and you will be expected to give a presentation/demonstration of your work, which will also be examined.

Knowledge & Reasoning

15 credits
Autumn teaching, Year 3

This module covers computational methods of knowledge representation and reasoning, tracing their origins in epistemology and the study of logic, and showing their evolution and use in artificial intelligence.

Advanced Natural Language Engineering

15 credits
Spring teaching, Year 3

Advanced Natural Language Engineering builds on the foundations provided by the Natural Language Engineering module, providing an opportunity to undertake a practical text-processing project involving a genuine natural language processing application scenario.

Under the supervision of a member of the Informatics Natural Language Processing Research team, you will spend the first three weeks identifying a suitable challenge to work on for your project, and then in the remainder of the term, design, implement and carefully evaluate a prototype piece of software.

The seminars will provide in-depth discussion of a number of general issues that arise when developing natural language processing tools, including: experimentation and hypothesis testing; advanced data smoothing techniques; domain adaptation; topic modelling; active learning; generative versus discriminative learning; and semi-supervised learning.

Current Issues in Cognitive Science

15 credits
Spring teaching, Year 3

This module familiarises you with topics at the leading edge of scientific and philosophical progress in the rapidly evolving area of cognitive science. It provides insights into the range of methods used to research those topics and the theories behind those methods. Drawing on previous cognitive science courses, this module looks to the future development of cognitive science and prepares you to participate in it.

Development of the Nervous System

15 credits
Spring teaching, Year 3

The human adult nervous system consists of a wide range of specialised cell types that make up the brain, central and peripheral nervous system, as well as specialised sensory organs such as the eye and ear. These different neuronal cell types arise from a common progenitor during development, and furthermore, many of the essential genetic elements required for their development have been retained across different species during evolution. This module will cover selected highlights of contemporary research findings from drosophila, chicken and mouse developmental biology that have informed our emergent understanding of the genes and cellular processes involved in nervous system development and organisation, that will likely impact on the ability to repair spinal cord injuries and treat neurodegenerative disorders in your generation.

Generative Creativity

15 credits
Spring teaching, Year 3

The module will introduce the use of generative creativity (GC) in a variety of areas selected from music, art, architecture, design, engineering and literature. The majority of the module will focus on examining a number of case studies that demonstrate the variety of approaches taken by existing GC systems. The module teaching is very much hands-on and programming is required. Although the module is not primarily philosophical in orientation, it will involve discussion on the nature of creativity, its definition and evaluation, by referring to the work of creativity researchers in a variety of disciplines from cognitive science to psychology.

Human-Computer Interaction

15 credits
Autumn teaching, Year 3

Human computer interaction (HCI) is concerned with understanding and designing interactive technologies from a people-centred perspective. This HCI module will give an introduction to the basic principles, methods and developments in HCI, with the objective of getting you to think constructively and analytically about how to design and evaluate interactive technologies, with opportunities to apply the principles and methods in practice. Topics include: principles of design, evaluating interactive technologies, understanding users, generating requirements, prototyping and iterative evaluation.

Intelligence in Animals and Machines

15 credits
Autumn teaching, Year 3

The module will develop your understanding of what it means for an animal or a machine to behave intelligently, and how brain and behavioural systems are adapted to enable an animal to cope effectively within its environment. You will consider diverse aspects of intelligence, including navigation and motor control, numerical, language, memory and social skills. You will explore how these are related to one another and how they are matched to the particular needs of animals and machines.

Multimedia Design and Applications

15 credits
Spring teaching, Year 3

Prerequisite: Java programming skills.

Computers now manipulate many more media than simple text and numbers. This module examines how modern computing systems manage, deliver and present multimedia such as audio, video, and interactive graphics. Topics include: information coding; multimedia hardware; networked multimedia; ergonomics; interface design; and multimedia applications.

Neural Networks

15 credits
Spring teaching, Year 3

To take this module you must already be able to write software in one appropriate programming language, such as Java, C, Python, or Matlab. Basic knowledge of formal computational skills is also a prerequisite.

In recent years neural computing has emerged as a practical technology with applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feed-­forward network architectures such as the multi­layer perceptron and the radial basis function network.

It is widely acknowledged that the successful application of neural computing requires a principled approach, and this module will make use of the recent advances in neural computing to explore neural networks in-depth. By concentrating on the pattern-recognition aspects of neural networks, the module will cover many important topics such as spiking neural networks, multi­layer perception, radial basis function network, support vector machines, competitive learning and independent component analysis. You will also learn to use neural networks in solving real world problems.

Real-World Cognition

15 credits
Autumn teaching, Year 3

This module enables students to recognise the achievements and utility of cognitive science and to apply its models and methods to real-world problems. Applications of cognitive science abound in the real world. For example, principles derived from cognitive science are applied to the design of information displays, educational technologies, safety equipment, etc. The module provides a framework for characterising different types of problem. Knowledge of research findings from cognitive studies of language, decision making, reasoning and problem solving can help people make better decisions, make them less susceptible to the bogus claims of some advertisements and to help them adopt a more rational stance in their perceptions of risk (e.g. in the context of gambling, `stranger danger', medical screening programmes, etc). Studies of complex problem solving give us insight into how expert performance differs from that of novices and how, for example, 'everyday' calculations in shops, markets and other real-world contexts differ from similar activities in formal educational settings. Understanding how language and cognition interact shows why some kinds of knowledge is difficult to acquire. Studies of human error show how everyday mistakes and slips occur and how they may be avoided or lessened. These are examples of the kinds of topics that can be approached from a cognitive science perspective.

Sensory and Motor Functions of the Nervous System

15 credits
Spring teaching, Year 3

Perceiving and acting upon the environment is something at which humans are expert. How does this ability to represent and act on visual and other sensory entities come about? Cognitive neuroscience is a diverse and interdisciplinary field of study that investigates the complex interplay of mental and brain function. This module provides an in-depth survey and analysis of behavioural observations, theoretical accounts, electrophysiological studies and imaging results on selected topics in cognitive neuroscience. Topics include: connections between sensory and motor function; cross-modal interaction; movement and event perception; development and plasticity of the nervous system; neural prediction and visual constancies; and compensation.

Social Cognitive Development

15 credits
Autumn teaching, Year 3

This module considers aspects of development that reflect the social nature of humans. You will cover three broad areas: social cognition (such as normal development of folk psychology and its apparent absence in autism), self­-reflective capabilities (such as the growth of self­-consciousness and shyness) and understanding aspects of the person (such as cross-­cultural concepts of the person and concepts of emotion). You will consider the role of cognitive development and social context in children's developing understanding of themselves and others. The module enables you to study a chosen topic in depth, while also making links to the other topics.

Structure and Function in the Brain

15 credits
Spring teaching, Year 3

Technology-Enhanced Learning Environments

15 credits
Spring teaching, Year 3

Topics in the Philosophy of Cognitive Science

15 credits
Autumn teaching, Year 3

This module examines various philosophical foundational issues in cognitive science by focussing on the nature and role of computation and representation in cognitive scientific explanations. In particular, the module asks the question: can our everyday way of understanding the mind, in terms of beliefs, desires and intentions, serve as a foundation for a scientific understanding of mind? The module then analyses various answers that have been given to this question.

Web 3D Applications

15 credits
Spring teaching, Year 3

Through lecture notes, demonstrations, surgeries (in class and online) and self directed e-learning and laboratory based tuition, this module will explore Web 3D technologies including but not limited to: 3D modelling methods and tools, navigation and interaction, web programming, etc. applied to the implementation of Web 3D applications. The main focus of this module is to gain practical experience on simple 3D modelling and programming (e.g. 3ds Max, pseudo 3D methods, X3D/VRML, HTML, XML, JavaScript, etc.) to build a Web 3D application (usually a small set of web pages with some 3D content). Example Web3D applications may include, but are not limited to, a virtual museum, car simulation, 3D product visualisation, burglary simulation or simple web game.

Web Applications and Services

15 credits
Spring teaching, Year 3

This module provides an introduction to the models and technologies used to provide distributed applications and services over the Internet. You will study the features and problems of building distributed applications, such as naming, security, synchronisation, replication, object persistence and content distribution. You will use the framework provided by the Java Enterprise Edition to build distributed web applications.

Web Computing

15 credits
Autumn teaching, Year 3

This module provides an introduction to the models and technologies used to provide services over the Internet and, in particular, the World Wide Web. Topics covered include: XML, including DTD, Schema, DOM, XPATH and XSLT, client-side programming (embedded scripting languages, style sheets), server-side programming (Java Servlets, JSP), and applications.