A practical introduction to computers and computer programming in a high-level language. The course is lab-based and focuses on reading and writing computer programs. The course is intended for students who may wish to advance in Computer Science or in Information Systems and Operations Management.
An introduction to the various layers that make up a modern computer system: encoding of data and instructions, hardware, low-level programming, operating systems, applications and communications.
A practical introduction to computing. Topics include: web design, an overview of computer hardware and operating systems, effective use of common applications, using the internet as a communication medium, applying programming concepts, and social implications of technology.
Basic mathematical tools and methods needed for computer science are introduced. Elementary mathematical skills for defining, analysing and reasoning with abstract objects used in programming are developed. Topics include integers and rational numbers, strings and sets, methods of proof (including induction), algorithms and functions, and elementary introductions to graphs, trees, counting and probability.
Fundamental programming techniques and processes, such as conditionals, iteration, recursion, functions, testing and debugging. Efficient ways to organise and manipulate data, including sorting and searching algorithms. Writing software that uses and implements common abstract data types such as lists, stacks, queues, dictionaries and trees.
The low level representation of data and algorithms in the computer. An introduction to computer organisation. The instruction execution model. Assembly and disassembly of instructions. Assembly language programming. How a high-level language is implemented at the machine level. The memory subsystem. Hardware support necessary to implement a secure multi-user operating system.
An introduction to data communications: the OSI reference model, particularly how the lower layers combine to implement the application layer. An introduction to secure communication and computer systems.
An introduction to the analysis of algorithms and data structures. Common abstract data types and their implementations. Asymptotic complexity analysis. Sorting and searching algorithms. Depth-first and breadth-first search and applications. Graph optimisation problems.
Science An introduction to the foundations of computer science, mathematics and logic. Topics include logic, principles of counting, mathematical induction, recursion, sets and functions, graphs, codes, and finite automata.
The design and implementation of object-oriented programmes. Analysis and design. Modelling with UML. Design for reuse, for testing, and for ease of change. Programming with classes, objects and polymorphism.
An introduction to software development, including processes, best practices, tools and quality assurance techniques such as testing.
An introduction to research topics in computer science. Students will be expected to prepare and deliver a review of research in a topic of their choice. Research articles will be provided during the course, and will consist of key scientific publications.
Modern processor architectures. Principles of modern processor design; pipelining; memory hierarchies; I/O and network interfacing; compiler and OS support; embedded processors; performance; multiprocessing.
The structure of data communications and networks, including the internet, covering all levels of the communications architecture. The layered protocol model, data transmission and coding, link-level and local area network protocols, wide-area internet working, routing, transport and security protocols. Basic application protocols as the foundation for distributed computing.
Introduces various concepts related to software, system and network security. Covers a range of topics including attacks on privacy and attack surface, static and dynamic analysis of malware, hardware security (trusted computing base, secure boot, and attestation), network security and some hot topics in cryptography including elliptic curve, blockchain and bitcoin.
Fundamental design techniques used for efficient algorithmic problem-solving and software development. Methods that yield algorithms that are both provably correct and efficient. Efficiency of algorithms to provide a basis for deciding which algorithm is best for the job. Limits on the power of computers and the theory of NP-completeness. An introduction to methods whose correctness or performance is not guaranteed.
Students will understand how to develop large-scale software systems, and learn about the issues associated with large-scale software systems and techniques for addressing them.
Covers web programming concepts, with applications to data integration from heterogeneous and asynchronous collections. Building web and cloud clients and services, with emphasis on high-level declarative and functional techniques. Dynamic web applications. Security and performance as overarching factors of web application development.
Operating system principles. Multi-user systems. Virtualisation. Scheduling. Concurrent processes, threads, synchronisation and deadlock. Memory allocation and virtual memory. Managing files, disks and other peripherals. Security, protection and archiving. Distributed systems and algorithms, location, migration and replication transparency. Real-time requirements. History of operating systems.
Human behaviour and humans' expectations of computers. Computer interfaces and the interaction between humans and computers. The significance of the user interface, interface design and user centred design process in software development. Interface usability evaluation methodologies and practice. Includes a group development and evaluation project using current implementation techniques and tools.
The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages (finite automata, context- free grammars, Turing and register machines) are studied. Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed.
Database principles. Relational model, Relational Algebra, Relational Calculus, SQL, SQL and Programming Languages, Entity Relationship Model, Normalisation, Query Processing and Query Optimisation, ACID Transactions, Transaction Isolation Levels, Database Recovery, Database Security, Databases and XML.
Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Covers algorithms and representational schemes used in artificial intelligence. AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks. Tasks such as game playing (adversarial search), planning, and natural language processing. Discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.
Many sciences use computational methods that involve the development and application of computer algorithms and software to answer scientific questions. This course looks at how to tackle these interdisciplinary problems through methods like probabilistic computer modelling, computer-based statistical inference, and computer simulations. The material is largely motivated by the life sciences but also uses examples from other sciences. It focuses on modelling and analysing real-world data with an emphasis on analysing DNA sequence data. No background in physical or life sciences is assumed.
Basic geometric processes including transformations; viewing and projection; back projection and ray tracing. Graphics modelling concepts: primitives, surfaces, and scene graphs, lighting and shading, texture mapping, and curve and surface design. Graphics and image processing fundamentals: image definition and representation, perception and colour models, grey level and colour enhancement, neighbourhood operations and filtering. Use of the OpenGL graphics pipeline.
Each student taking one of these courses will be expected to do an individual practical project under the supervision of a member of staff. Only students with excellent academic records will be allowed to take these courses, and only after a supervisor and topic have been agreed upon by the Head of Department.
An overview of research methods and techniques used across the discipline of Computer Science, including formal proof techniques and empirical methods that involve quantitative and/or qualitative data. Students will be expected to apply the research methods in a collaborative research project.
Students work in small groups to complete a substantial problem applying the knowledge learnt from the different courses in the Computer Science major. Teams are expected to reason on a problem, devise a solution, produce an artefact and present their work. The capstone provides an opportunity for students to further develop their technical and communication skills.
Developing maintainable software has been an ongoing challenge in the software industry. This course presents the principles and practices that have been proposed for developing maintainable software systems. It will evaluate and critique these principles and practices through examining their application in practice and through understanding the research on their effectiveness.
Covers security features supported by the different platforms for smart devices. Provides an overview of the most popular OS platforms in the market and focuses on security for Android and iOS. Recommended preparation: COMPSCI 340
AI deep learning has significantly advanced image understanding, language modelling, speech recognition, game playing, and more. These developments enable near-human capabilities in text and image generation. Explores highly specialised knowledge in planning, reasoning, explanation, natural language understanding, and knowledge acquisition, and assess their contribution to highly competent, general AI systems. Includes a significant individual research project.
Human behaviour and humansβ expectations of interaction. Computer interfaces and the interaction between humans and computers. The significance of the user interface, interface design and user centred design process in system development. Interface usability evaluation methodologies and practice. Includes processes of evaluation, design, and implementation using current techniques and tools.
Human aspects of computer systems, relevant to commercial solution development and computer science research. Sample topics: advanced evaluation methods; support of pen and touch-based interaction; trends with domain specific user interface design, such as interfaces for enterprise systems.
Computer architectures and languages for exploring parallelism, conceptual models of parallelism, principles for programming in a parallel environment, different models to achieve interprocess communication, concurrency control, distributed algorithms and fault tolerance. Recommended preparation: COMPSCI 320 or 335
Introduces students to a range of philosophical and normative topics relating to artificial intelligence. Examines key ideas of intelligence, privacy, consent, and discusses other ethical issues that arise in the development and use of AI. The importance of MΔori rights and interests in AI and data are explored. Possible approaches to addressing these various concerns are considered.
Examines the core concepts and techniques in AI, including breakthroughs in symbolic AI, machine learning, and neural networks. Real-world applications are presented, with a focus on AI research in Aotearoa/NZ and ethical considerations. The course is designed to be accessible to students with limited programming experience.
Equips students with the ability to develop AI applications by introducing well-established AI frameworks and using web-based interactive computing platforms. Students will acquire the skills to implement simple AI techniques using these frameworks and evaluate their performance. Introduces basic practical technologies to investigate artificial intelligence techniques.
An advanced look at current research issues in computer graphics. Typical topics include: ray-tracing acceleration methods; radiosity; subdivision surfaces; physically-based modelling; animation; image-based lighting and rendering; non-photorealistic rendering; advanced texturing.
Fundamental techniques are covered for the design of algorithms such as greedy algorithms, divide-and-conquer, and dynamic programming. Data structures are explored that help implement algorithms. Essential tools are taught for analysing algorithms, for example worst- and average-case analyses of space and time. Recommended preparation: COMPSCI 120, 130
An examination of object-oriented programming and design. Key principles of object-oriented programming: typing, encapsulation, inheritance, polymorphism and composition. Fundamental object-oriented modelling and design techniques. Students will develop application software of reasonable complexity that draws on object- oriented language features, and contemporary APIs, frameworks and tools.
An examination of developing web-based applications. Client-side technologies: HTML, CSS and Javascript. Server- side technologies to support dynamic Web pages and data access. Fundamental relational database concepts and design techniques. Principles of Web-application design. HCI considerations and mobile clients. Students will build a Web-based application that dynamically generates content involving relational database access.
Selected advanced topics in design and analysis of algorithms, such as: combinatorial enumeration algorithms; advanced graph algorithms; analytic and probabilistic methods in the analysis of algorithms; randomised algorithms; methods for attacking NP-hard problems.
Randomised algorithms are algorithms that βflip coinsβ to make decisions. In many cases, such algorithms are faster, simpler, or more elegant than the classical, deterministic ones. Covers basic principles and techniques used to design and analyse randomised algorithms, and applications of randomised methods in mathematics and computer science. Recommended preparation: STATS 125, COMPSCI 225 or MATHS 254, COMPSCI 320
The human aspect of cyber security and privacy engineering is relevant to commercial solution development and cyber security and privacy research. Sample topics: secure systems design; usable security systems evaluation; privacy-preserving software systems; threat modelling; economics of usable security and privacy; OWASP Top 10 vulnerabilities. Recommended preparation: 30 points from COMPSCI 313, 314, 320, 335, 340, 351, 702, 734, 742
Focuses on the use and deployment of protective systems used in securing internal and external networks. Examines in detail the widely used protocols including SSL, IPSec, DNSSEC as well as covers infrastructure platform protocols including wireless security (IEEE 802.11). Explores current research and developments in the area of network defence and countermeasures. Recommended preparation: COMPSCI 314, 315
Builds on best practices, and compliance standards to establish an advanced understanding of modern cryptographic systems used in securing communications and data storage. Advanced knowledge in modern cryptography management issues such as algorithm selection, generation, distribution, and revocation of encryption keys are applied through a research-based report and a group project. Recommended preparation: COMPSCI 210 or MATHS 120
An advanced course examining research issues related to tools and techniques for software design and development. Topics include: techniques for data mapping and data integration, software architectures for developing software tools, issues in advanced database systems. Recommended preparation: COMPSCI 331 or SOFTENG 325 or COMPSCI 718 and 719
Examines advanced and emerging software architectures at the confluence of XML, web services, distributed systems, and databases. Includes advanced topics in areas such as: mobile computing, remoting, web services for enterprise integration, workflow orchestrations for the enterprise, peer-to-peer computing, grid computing. Recommended preparation: COMPSCI 335 or 718
The course covers wide area networks, global routing, network and protocol performance, buffering and queuing, advanced network measurement, network application performance, content networks, and advanced networking concepts. Recommended preparation: COMPSCI 314 or 315
An overview of topics related to the use of technology in education and how people learn computer science concepts. Topics include research methodologies used in computer science education, how novices learn to program, and how technology can engage students in active learning, facilitate collaboration and enhance traditional educational practice. Recommended preparation: 30 points at Stage III
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350
Database principles. Relational model, relational algebra, relational calculus, SQL, SQL and programming languages, entity-relationship model, normalisation, query processing and query optimisation, ACID transactions, transaction isolation levels, database recovery, database security, databases and XML. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225 or COMPSCI 718
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 220, 351
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation- based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.
Examines the cornerstones of AI: representation, utilisation, and acquisition of knowledge. Taking a real-world problem and representing it in a computer so that the computer can do inference. Utilising this knowledge and acquiring new knowledge is done by search which is the main technique behind planning and machine learning. Research frontiers in artificial intelligence.
Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Students will be introduced to the foundations of machine learning and will gain practical skills to solve different problems. Students will explore research frontiers in machine learning.
Critically analyses the fundamentals of deep neural networks alongside current state-of-the-art advancements in this field. Students will acquire specialised knowledge in state-of-the-art deep learning architectures and gain the ability to apply deep learning in various fields, including natural language processing and computer vision. Includes a significant individual research project.
How can researchers of artificial intelligence effectively model subjective aspects of minds, such as emotional states, desires, perceptual experience and intrinsic goals? This course draws upon interdisciplinary methods and considers classic and emerging approaches to try to answer this question. Recommended preparation: COMPSCI 367
An introduction to the design, implementation and use of intelligent software agents (e.g., knowbots, softbots etc). Reviews standard artificial intelligence problem- solving paradigms (e.g., planning and expert systems) and knowledge representation formalisms (e.g., logic and semantic nets). Surveys agent architectures and multi- agent frameworks.
Examines the progress in enabling AI systems to use natural language for communication and knowledge storage. Explores knowledge formalisation, storage, multiple knowledge systems, theory formation, and the roles and risks of belief, explanation, and argumentation in AI. Includes a significant individual research project.
Processing Basic geometric processes including transformations; viewing and projection; back projection and ray tracing. Graphics modelling concepts: primitives, surfaces, and scene graphs, lighting and shading, texture mapping, and curve and surface design. Graphics and image processing fundamentals: image definition and representation, perception and colour models, grey level and colour enhancement, neighbourhood operations and filtering. Use of the OpenGL graphics pipeline. Research frontiers in computer graphics and image processing. Recommended preparation: COMPSCI 210, 230
Computational methods and techniques for computer vision are applied to real-world problems such as 2/3D face biometrics, autonomous navigation, and vision-guided robotics based on 3D scene description. A particular feature of the course work is the emphasis on complete system design. Recommended preparation: COMPSCI 373 and 15 points at Stage II in Mathematics
Enables the development of practical knowledge and hands-on experience through a supervised internship in the IT industry. Students complete a research-informed project, and present both written and oral reports of their findings.
Enables students to gain workplace experience, the development of practical knowledge, and hands-on experience on research-informed AI projects through a supervised internship with an external organisation. At the end of the internship, students are expected to present both written and oral reports of their findings.