Search Course Outline

Search

Showing 25 course outlines from 4474 matches

1076

COMPSCI 767

: Intelligent Software Agents
2021 Semester One (1213)
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. Recommended preparation: COMPSCI 367.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
1077

COMPSCI 767

: Intelligent Software Agents
2020 Semester One (1203)
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. Recommended preparation: COMPSCI 367.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
1078

COMPSCI 769

: Natural Language Processing
2025 Semester Two (1255)
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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1079

COMPSCI 769

: Natural Language Processing
2024 Semester Two (1245)
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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1080

COMPSCI 773

: Intelligent Vision Systems
2025 Semester One (1253)
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
Subject: Computer Science
No pre-requisites or restrictions
1081

COMPSCI 773

: Intelligent Vision Systems
2024 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
Subject: Computer Science
No pre-requisites or restrictions
1082

COMPSCI 773

: Intelligent Vision Systems
2023 Semester One (1233)
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.
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
1083

COMPSCI 773

: Intelligent Vision Systems
2022 Semester One (1223)
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.
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
1084

COMPSCI 773

: Intelligent Vision Systems
2021 Semester One (1213)
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.
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
1085

COMPSCI 780

: Postgraduate Project in Computer Science 1
2024 Semester One (1243)
Subject: Computer Science
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
1086

COMPSCI 780

: Postgraduate Project in Computer Science 1
2021 Semester Two (1215)
Subject: Computer Science
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
1087

COMPSCI 780

: Postgraduate Project in Computer Science 1
2021 Semester One (1213)
Subject: Computer Science
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
1088

COMPSCI 780

: Postgraduate Project in Computer Science 1
2021 Summer School (1210)
Subject: Computer Science
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
1089

COMPSCI 780

: Postgraduate Project in Computer Science 1
2020 Semester Two (1205)
Subject: Computer Science
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
1090

DATASCI 100

: Data Science for Everyone
2025 Semester Two (1255)
Explores how to use data to make decisions through the use of visualisation, programming/coding, data manipulation, and modelling approaches. Students will develop conceptual understanding of data science through active participation in problems using modern data, hands-on activities, group work and projects. DATASCI 100 will help students to build strong foundations in the science of learning from data and to develop confidence with integrating statistical and computational thinking.
Subject: Data Science
No pre-requisites or restrictions
1091

DATASCI 399

: Capstone: Creating Value from Data
2022 Semester Two (1225)
A group-based project in which students showcase their skills in collaboratively creating value from data. Within a given data science domain, teams will jointly develop a research question, apply their skills to gather, structure, and analyse data to address the question, and communicate their findings effectively. The insights, their implications, limitations, and future work will be discussed by the group. Each team member will write an individual report about the project.
Subject: Data Science
Prerequisite: 30 points at Stage III in Data Science
1092

DATASCI 709

: Data Management
2025 Semester One (1253)
Data management is the practice of collecting, preparing, organising, storing, and processing data so it can be analysed for business decisions. The course will use R and SQL to illustrate the process of data management. This will include principles and best practice in data wrangling, visualisation, modelling, querying, and updating.
Subject: Data Science
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: COMPSCI 351, 751, STATS 383, 707, 765
1093

DATASCI 709

: Data Management
2024 Semester One (1243)
Data management is the practice of collecting, preparing, organising, storing, and processing data so it can be analysed for business decisions. The course will use R and SQL to illustrate the process of data management. This will include principles and best practice in data wrangling, visualisation, modelling, querying, and updating.
Subject: Data Science
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: COMPSCI 351, 751, STATS 383, 707, 765
1094

DATASCI 779

: Statistical Computing Skills for Professional Data Scientists
2025 Semester One (1253)
Fundamental topics taught in statistical computing and data management including use of data analytic software such as Excel and R for data analysis, programming, graphics, cleaning and manipulating data, use of regular expressions, mark-up languages LaTeX, and R Markdown, use of SQL and DBMSs, reproducible research and symbolic computation. Students will undertake assigned individual research projects to be presented in-class.
Subject: Data Science
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
Restriction: STATS 779
1095

DATASCI 792

: Dissertation
2020 Semester Two (1205)
Subject: Data Science
To complete this course students must enrol in DATASCI 792 A and B, or DATASCI 792
1096

DATASCI 792

: Dissertation
2020 Semester One (1203)
Subject: Data Science
To complete this course students must enrol in DATASCI 792 A and B, or DATASCI 792
1097

DATASCI 792A

: Dissertation
2020 Semester Two (1205)
Subject: Data Science
To complete this course students must enrol in DATASCI 792 A and B, or DATASCI 792
1098

DATASCI 792A

: Dissertation
2020 Semester One (1203)
Subject: Data Science
To complete this course students must enrol in DATASCI 792 A and B, or DATASCI 792
1099

EARTHSCI 102

: Foundation for Earth Sciences
2020 Semester Two (1205)
Exploring and understanding the complexities of Earth systems requires earth scientists to engage with a range of quantitative techniques and tools. Introduces students to contemporary approaches for analysing and interpreting earth science data. Covers mathematical, physical, computational, and chemical methods used in the earth sciences. Emphasises practical application to a variety of earth science topics.
Subject: Earth Sciences
Restriction: EARTHSCI 263
1100

EARTHSCI 105

: Earth’s Natural Hazards
2025 Semester Two (1255)
New Zealand experiences many natural hazards caused by the Earth’s natural processes through earthquakes, volcanic eruptions, weather bombs, storm surge, tsunami, flooding and wildfires. Focuses on spatial and temporal occurrences of disasters, hazard preparedness and recovery, and societal responses that affect and, sometimes, compound the magnitude of disasters. Case studies are drawn from contemporary and ancient societies.
Subject: Earth Sciences
No pre-requisites or restrictions