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876

COMPSCI 765

: Modelling Minds
2022 Semester One (1223)
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.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
877

COMPSCI 765

: Interactive Cognitive Systems
2021 Semester One (1213)
Many aspects of intelligence involve interacting with other agents. This suggests that a computational account of the mind should include formalisms for representing models of others' mental states, mechanisms for reasoning about them, and techniques for altering them. This course will examine the role of knowledge and search in these contexts, covering topics such as collaborative problem solving, dialogue processing, social cognition, emotion, moral cognition, and personality, as well as their application to synthetic characters and human-robot interaction.
Subject: Computer Science
Recommended preparation: COMPSCI 367 Prerequisite: Approval of the Academic Head or nominee
878

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
879

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
880

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
881

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
882

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
883

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
884

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
885

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
886

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
887

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
888

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
889

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
890

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
891

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
892

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
893

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
894

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
895

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
896

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
897

EARTHSCI 105

: Earth’s Natural Hazards
2024 Semester Two (1245)
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
898

EARTHSCI 105

: Earth’s Natural Hazards
2024 Semester One (1243)
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
899

EARTHSCI 105

: Earth’s Natural Hazards
2024 Summer School (1240)
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
900

EARTHSCI 105

: Earth’s Natural Hazards
2023 Semester Two (1235)
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