BSc (Hons) Computer Science (Artificial Intelligence and Data Science)

This module will introduce you to the fundamentals of programming and object orientation, including the following basic ideas of programming:

• variables
• memory and assignment statements
• control through conditional statements, loops
• functions and procedures
• objects and classes
• instance variables and methods
• arrays
• user interaction
• interaction between objects. 

Designing and creating an effective website can showcase a wide range of the technical and human skills required by computing professionals from coding basic HTML to empathizing with a diverse range of users and stakeholders.

This module will introduce you to the basic techniques of web development in a highly human-centred context that encourages students to think about the lived experience of people using technologies, particularly those who are very different from themselves. This work culminates in the development of an interactive website designed for a particular user group.

This module will give you a foundation in the fundamental workings of computer systems and how they represent data. This stems from fundamental mathematical principles such as binary number bases and Boolean logic and applies these to the design of computing hardware and finally to data representation in the form of a wide variety of formats. You'll end with a taste of the most cutting-edge uses of data in the form of a first introduction to machine learning, the foundation of modern Artificial Intelligence.

This module is aimed at novice programmers who have learnt some basic programming techniques. The course builds on this knowledge by developing your ability to combine short segments of code to create larger projects. 

You'll begin by developing their knowledge of object-oriented programming through learning about principles such as encapsulation and abstraction. You'll be presented with existing code and designs which you'll explore, complete and debug. You'll build upon an example application in the development of a final project.

You'll be expected to engage in both the technical and social implications of your work, considering the applications applicability to a human context. This may include the ethical and cultural impact of software. 

During this work, you'll apply taught techniques in organising, planning and evaluating your code.  

In this module, you'll explore the perceptual and technical fundamentals of graphics computing.  

Topics include: 

• fundamentals of visual perception: basic colour theory, perspective, and animation 
• manipulating images for creative contexts 
• image processing 
• the application of 2D and 3D geometry for animation and interaction 
• creating simple physics simulations. 

You'll apply this knowledge through a series of practical and creative exercises undertaken throughout the module using an appropriate programming environment with supporting graphics libraries.

This module will introduce you to the analysis and design of algorithms. In particular, you'll learn how to classify algorithms in terms of their computational resource consumption, how to solve efficiently classical problems from computer science and how to apply design techniques to build new algorithms.

In this module, subtitled ‘Everything is a Text’, you will consider the value of different types of texts and ways of imparting knowledge and ideas. You will reflect upon your identities as learners and future professionals in the world, considering a range of contexts: the academic/educational context, personal settings and the eco-systems that you live and work in. These reflections will be used to inform your practices as academic learners.

You will explore academic literacies, different ways of knowing and consider what counts as ‘legitimate’ knowledge. You will engage with critical thinking, making arguments and establishing criteria to defend intellectual positions and these skills will be acknowledged as social practices that produce and reinforce meaning and frameworks of understanding and knowledge.

Furthermore, you will engage with a wide range of academic and non-academic material, individuals and environments in order to contribute to discussions regarding attitudes and assumptions about ideas and experience, including within labour markets, cultural hegemonies, distributions of power and the relationship between the individual and society. In this way, the social interactions, relationships and contexts that underpin academic literacies in higher education will be made explicit.

You will discuss these ideas with students and tutors from the different subjects at Goldsmiths, and learn to be part of the wider university community. You will also be able to submit an assignment which could be a written, graphically designed, audio, video, or negotiated project. You will get to choose the assessment that best shows what you can do.

This module, subtitled ‘Researching Our World & Lives’, builds on the conceptual and contextual foundations of Identity, Agency and Environment 1.

You will learn how to conduct academic research and will be offered the opportunity to broaden and deepen your understanding of the relationship between your own interests, skills, values, career and non-career aspirations, the concepts, theories and contexts of your discipline, and the world.

You will reflect upon your identities as researchers, and learn how the research skills you’ve acquired both within your studies and the world more generally can be related to problem-solving in a wide range of contexts. You will consider your agency as researchers, what you can and cannot research, the ethical issues involved, and think reflexively about your position as a researcher in a range of environments and eco-systems.

Formal conventions of academic research and writing will be integrated into your individualised contexts and goals, enabling the expression of ideas and perspectives that may challenge the status quo. The module will encourage creativity, activism, decision-making and the formation of judgements leading to action-planning in relation to research topics and types of evidence, and professional planning.

You will learn to critique your own subject disciplines. Interdisciplinary sharing of knowledge will ensure that assessment and learning practices provide you with the opportunity to develop new lines of thinking and knowing, within formative collaborative learning and research communities.

This module aims to advance your software development skills so that you can write more robust and complicated programs. You’ll learn how to use a range of programming techniques that will allow you to deal with unwanted or unexpected events that might happen when your application is running.

You’ll use defensive coding to check data before processing it, and exception handling to gracefully manage unforeseen or unwanted occurrences. You’ll learn how to discuss program structure concerning cohesion (how to meaningfully organise code into modules) and coupling (how to define the interactions between different parts of the program). You’ll learn about test-driven development, where you write tests for your code, and write the code itself, in parallel. You will also learn how to use software versioning tools to manage a software project as it develops.

This module will give students the opportunity to experience group work, in the context of projects to specify, develop, deploy and evaluate a computer based system. This module will take students through the entire process, from requirements gathering, user-centred design, proposal development, implementation and evaluation. Students will also assess their outputs from the perspective of multiple roles within an organization, and consider the potential impact of their system on customers, users and society at large.

This module will introduce you to Java for the first time whilst building on the programming techniques covered in your first year of study. Through learning about more advanced concepts within object orientation, you'll be able to design and implement large-scale computer programs. 

Topics covered include Types, Conditionals and Iteration, Methods, Exception handling, I/O, Classes, Inheritance, Abstract Classes and Interfaces. Throughout the module, there will be a focus on developing your skills in problem-solving through structured thought and familiarity with common resources such as IDEs, professional APIs and language specifications.

Increasingly, computer systems in research and industry, and particularly the machine learning systems that underpin modern Artificial Intelligence, are designed to leverage large amounts of data.

This data is rich and various and may include anything from the results of clinical trials to information gleaned from analysing millions of tweets to understanding how people talk positively and negatively about politics.

In this module, you'll learn how to develop systems that operate in, and make use of such data-rich environments. This module builds on other material in the programme such as mathematics and databases. 

This module will show you how to work with data in various ways:

• Capturing data from a variety of sources
• Visualising data in compelling, informative ways
• Processing data to make it useful and shareable
• Reasoning with data to test hypotheses and make parameterised predictions

You'll also implement and apply basic machine learning methods. The module will also introduce you to a new language and programming environment that is well-adapted to languages for these applications.

This module aims to expose students to standard data structures and algorithms for manipulating them. In particular, it will give students the chance to learn to choose appropriate data structures for solving problems.

Our academic departments are developing exciting elective ideas to allow you to broaden your education, either to develop vocationally orientated experiences or to learn more about contemporary society, culture and politics. You’ll be able to choose safe in the knowledge that these modules have been designed for non-subject specialists and to bring students from different disciplines together. For example, you may want to take introductions to areas such as Law, Education, the digital industries, the creative industries,think like a designer or understand the history and politics behind our current affairs.

All aspects of our lives are increasingly being underpinned by a computational infrastructure of devices, networks and servers. This module will give you an overall understanding of these technologies and how they are used in practice. These include the components and functions of mobile and desktop operating systems, operating systems processes and scheduling, network architectures and protocols and serverside computation and cloud computing.

This module will position these technologies within the human and organizational context of their use and will culminate in a project in which you will design a computational infrastructure for a particular organisation or application.

This module aims to provide you with an understanding of the need for computer security and the technologies that support it. It has both a theoretical component that will teach you mathematical underpinnings of security systems and a practical element that will help you discover the pitfalls of security design and to comprehend the mathematics underlying the protocols by programming small examples.

Lots of students join Goldsmiths because they want to make a difference in society, to bring about positive change and develop skills and experiences which will allow them to access exciting careers. Goldsmiths’ Social Change module will allow you to do work on group projects with students from other departments to bring about change. You’ll be introduced to the UN’s Sustainable Development goals and core project management theories and practices allow you to work across a number of weeks towards a final Festival of Ideas where you’ll report work back to the academic and local community.

Final Project in Computer Science is an opportunity for students to apply the skills, knowledge and expertise that they have acquired whilst studying Computer Science to a single and coherent body of work. The project allows students to follow an initiative that appeals to them; the outcome will be the most complex, detailed and thorough system that they have had to develop as part of their studies. Implemented systems may comprise a software application or a combination of hardware and software development. The project outcomes will serve as a showcase for student’s talents and could launch a professional career in industry.

Artificial Intelligence is a broad discipline and includes many ideas and techniques. This module will focus on the latest developments in this rapidly expanding field. The emphasis is on solving real-world problems and building creative applications with state-of-the-art technology.  

Topics will vary, and may include deep learning, generative deep learning, fully and partially observable environments, environments containing other artificial intelligences, deterministic, stochastic and dynamic environments, problem solving as search, inductive, deductive and probabilistic reasoning, knowledge representation and learning, philosophical and moral dimensions, challenges for society, possibilities and AI history.

Neural Networks are widely used techniques for modelling and classifying data. They are used in industry for data analysis applications such as image classification, speech analysis and regression tasks.

This module will provide you with specialised theoretical and practical knowledge of a range of Neural Network architectures that are appropriate for data-oriented tasks. This module is complementary to the Machine Learning and Artificial Intelligence modules in the programme, focusing on the area of neural computation.  

You'll be introduced to the theory and practice of neural computation. You'll learn the principles of neuro-computing with Neural Networks widely used for addressing real-world problems such as regression, pattern recognition and time-series prediction. 

This module gives you an introduction to machine learning and statistical learning. Topics include supervised learning (regression and classification as parametric and non-parametric methods), and unsupervised learning, but discusses also semi-supervised learning, reinforcement learning, and elements of the learning theory.

You'll explore recent applications of machine learning and deep learning by illustrating practically, using modern software libraries, how machine learning algorithms and methods are applied effectively, and how models’ performances can be optimised using real-world datasets. Ethical aspects related to machine learning are also discussed.

The module introduces you to data mining techniques and methods utilised in the process of discovering patterns in data generated in various fields such as business, financial, social, medical etc., that are abundantly available nowadays.

You'll learn practical skills through data mining algorithm implementation, and through conducting knowledge discovery in data with specialised software and libraries. You'll explore the applicability of data mining techniques in areas such as text mining and sentiment analysis, financial applications and credit scoring, prediction modelling in health.