Search Course Outline
Showing 25 course outlines from 1580 matches
276
COMPSCI 373
: Computer Graphics and Image Processing2024 Semester One (1243)
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.
Prerequisite: COMPSCI 210, 230, or COMPSYS 201 and SOFTENG 281
Restriction: COMPSCI 771
Restriction: COMPSCI 771
277
COMPSCI 399
: Capstone: Computer Science2024 Semester One (1243)
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.
Prerequisite: 30 points at Stage III in Computer Science and COMPSCI 210, 220, 230
278
COMPSCI 711
: Parallel and Distributed Computing2024 Semester One (1243)
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
No pre-requisites or restrictions
279
COMPSCI 712
: AI Agency, Ethics and Society2024 Semester One (1243)
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 Māori rights and interests in AI and data are explored. Possible approaches to addressing these various concerns are considered.
No pre-requisites or restrictions
280
COMPSCI 713
: AI Fundamentals2024 Semester One (1243)
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.
No pre-requisites or restrictions
281
COMPSCI 714
: AI Architecture and Design2024 Semester One (1243)
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.
No pre-requisites or restrictions
282
COMPSCI 717
: Fundamentals of Algorithmics2024 Semester One (1243)
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
Restriction: COMPSCI 220, 320, SOFTENG 250, 284
283
COMPSCI 718
: Programming for Industry2024 Semester One (1243)
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.
No pre-requisites or restrictions
284
COMPSCI 719
: Programming with Web Technologies2024 Semester One (1243)
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.
No pre-requisites or restrictions
285
COMPSCI 720
: Advanced Design and Analysis of Algorithms2024 Semester One (1243)
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. Recommended preparation: COMPSCI 320
No pre-requisites or restrictions
286
COMPSCI 727
: Cryptographic Management2024 Semester One (1243)
Focuses on cryptographic systems used in securing communications and data storage. Provides an overview of encryption algorithms including symmetric key cryptography, public key infrastructure, digital signatures and certificate technologies. The course covers management issues related to cryptography and explores current research and developments in this area. Recommended preparation: COMPSCI 210 or MATHS 120
No pre-requisites or restrictions
287
COMPSCI 732
: Software Tools and Techniques2024 Semester One (1243)
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
Restriction: SOFTENG 750
288
COMPSCI 747
: Computing Education2024 Semester One (1243)
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 in Computer Science or COMPSCI 718
No pre-requisites or restrictions
289
COMPSCI 751
: Advanced Topics in Database Systems2024 Semester One (1243)
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
Restriction: COMPSCI 351, SOFTENG 351
290
COMPSCI 752
: Big Data Management2024 Semester One (1243)
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
No pre-requisites or restrictions
291
COMPSCI 760
: Advanced Topics in Machine Learning2024 Semester One (1243)
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.
Prerequisite: COMPSCI 361 or 762
292
COMPSCI 762
: Foundations of Machine Learning2024 Semester One (1243)
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.
Prerequisite: COMPSCI 713 and 714, or COMPSCI 718, or 15 points from DATASCI 100, STATS 101, 108 and COMPSCI 220 or 717 and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 361
Restriction: COMPSCI 361
293
COMPSCI 765
: Modelling Minds2024 Semester One (1243)
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
No pre-requisites or restrictions
294
COMPSCI 773
: Intelligent Vision Systems2024 Semester One (1243)
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
No pre-requisites or restrictions
295
COMPSCI 780
: Postgraduate Project in Computer Science 12024 Semester One (1243)
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 691 To complete this course students must enrol in COMPSCI 780 A and B, or COMPSCI 780
Restriction: COMPSCI 691 To complete this course students must enrol in COMPSCI 780 A and B, or COMPSCI 780
296
COMPSYS 201
: Fundamentals of Computer Engineering2024 Semester One (1243)
Digital systems and binary coding; binary numbers; Boolean algebra and computer logic; combinational logic circuits; sequential logic circuits; hardware description language; digital design flow; register transfer level descriptions and design; data paths and control units; from circuits to microprocessors; basic computer organisation; introduction to modern microprocessors; timers and interfacing; C and assembly language for microprocessors; designing digital systems using microprocessors.
Prerequisite: ELECTENG 101
297
COMPSYS 302
: Design: Software Practice2024 Semester One (1243)
A project-based course to gain experience in software design emphasising problem solving techniques and applications in computer systems engineering. The course includes practical, real-world project(s) involving a representative subset of the following topics: algorithm and data structure selection and implementation, parsing and translation, object-oriented and multi-threaded programming, scripting languages, peer-to-peer communication over internet.
Prerequisite: COMPSYS 202 or SOFTENG 281
298
COMPSYS 305
: Digital Systems Design2024 Semester One (1243)
Digital Systems implementation technologies with emphasis on hardware description languages and design abstraction levels; structural, architectural and behavioural modelling; register-transfer level design; datapath and control units; functional and timing simulations; FPGA-based implementation design flow and case studies.
Prerequisite: COMPSYS 201
299
COMPSYS 700A
: Research Project2024 Semester One (1243)
Students are required to submit a report on project work carried out on a Computer Systems Engineering topic assigned by the Head of Department. The work shall be supervised by a member of staff.
Prerequisite: COMPSYS 301, and 45 points from COMPSCI 313, COMPSYS 302-305, ELECTENG 303, 331, 332
Restriction: COMPSYS 401 To complete this course students must enrol in COMPSYS 700 A and B
Restriction: COMPSYS 401 To complete this course students must enrol in COMPSYS 700 A and B
300
COMPSYS 701
: Advanced Digital Systems Design2024 Semester One (1243)
Advanced concepts in digital design including: System-on-Chip (system level description, behavioural and register-transfer descriptions); advanced modelling techniques and design flows; design space exploration and optimisation; hardware-software partitioning and trade-offs; component reusability; reconfigurable systems; low-power systems; case studies (speech, image, video algorithms implementation, application specific processor design); individual research projects to analyse the problem, model and implement the required hardware-software components.
Prerequisite: COMPSYS 305