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Showing 25 course outlines from 4473 matches
1051
COMPSCI 762
: Foundations of Machine Learning2023 Semester One (1233)
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 220 or 717, and 15 points from DATASCI 100, STATS 101, 108, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 361
Restriction: COMPSCI 361
1052
COMPSCI 762
: Foundations of Machine Learning2022 Semester One (1223)
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 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 361
Restriction: COMPSCI 361
1053
COMPSCI 762
: Advanced Machine Learning2021 Semester One (1213)
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 should understand the foundations of machine learning, and introduce practical skills to solve different problems. Students will explore research frontiers in machine learning. Recommended preparation: COMPSCI 220, 225 and STATS 101
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 361
Restriction: COMPSCI 361
1054
COMPSCI 762
: Advanced Machine Learning2020 Semester One (1203)
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 should understand the foundations of machine learning, and introduce practical skills to solve different problems. Students will explore research frontiers in machine learning. Recommended preparation: COMPSCI 220, 225 and STATS 101
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 361
Restriction: COMPSCI 361
1055
COMPSCI 764
: Deep Learning2025 Semester Two (1255)
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.
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1056
COMPSCI 764
: Deep Learning2024 Semester Two (1245)
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.
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1057
COMPSCI 765
: Modelling Minds2025 Semester One (1253)
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
1058
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
1059
COMPSCI 765
: Modelling Minds2023 Semester One (1233)
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.
Prerequisite: Approval of the Academic Head or nominee
1060
COMPSCI 765
: Modelling Minds2022 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.
Prerequisite: Approval of the Academic Head or nominee
1061
COMPSCI 765
: Interactive Cognitive Systems2021 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.
Recommended preparation: COMPSCI 367
Prerequisite: Approval of the Academic Head or nominee
1062
COMPSCI 767
: Intelligent Software Agents2025 Semester One (1253)
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.
Prerequisite: COMPSCI 367 or 761, or COMPSCI 713 and 714
1063
COMPSCI 767
: Intelligent Software Agents2021 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.
Prerequisite: Approval of the Academic Head or nominee
1064
COMPSCI 767
: Intelligent Software Agents2020 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.
Prerequisite: Approval of the Academic Head or nominee
1065
COMPSCI 769
: Natural Language Processing2025 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.
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1066
COMPSCI 769
: Natural Language Processing2024 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.
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
1067
COMPSCI 773
: Intelligent Vision Systems2025 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
No pre-requisites or restrictions
1068
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
1069
COMPSCI 773
: Intelligent Vision Systems2023 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.
Prerequisite: Approval of Academic Head or nominee
1070
COMPSCI 773
: Intelligent Vision Systems2022 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.
Prerequisite: Approval of Academic Head or nominee
1071
COMPSCI 773
: Intelligent Vision Systems2021 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.
Prerequisite: Approval of Academic Head or nominee
1072
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
1073
COMPSCI 780
: Postgraduate Project in Computer Science 12021 Semester Two (1215)
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
1074
COMPSCI 780
: Postgraduate Project in Computer Science 12021 Semester One (1213)
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
1075
COMPSCI 780
: Postgraduate Project in Computer Science 12021 Summer School (1210)
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
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