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Showing 25 course outlines from 2938 matches
676
COMPSCI 760
: Advanced Topics in Machine Learning2023 Semester Two (1235)
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
677
COMPSCI 760
: Advanced Topics in Machine Learning2023 Semester One (1233)
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
678
COMPSCI 760
: Machine Learning2022 Semester Two (1225)
An overview of the learning problem and the view of learning by search. Covers 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. Experimental methods necessary for understanding machine learning research.
Prerequisite: COMPSCI 361 or 762
679
COMPSCI 760
: Datamining and Machine Learning2021 Semester Two (1215)
An overview of the learning problem and the view of learning by search. 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. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
680
COMPSCI 760
: Datamining and Machine Learning2021 Semester One (1213)
An overview of the learning problem and the view of learning by search. 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. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
681
COMPSCI 760
: Datamining and Machine Learning2020 Semester Two (1205)
An overview of the learning problem and the view of learning by search. 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. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
682
COMPSCI 760
: Datamining and Machine Learning2020 Semester One (1203)
An overview of the learning problem and the view of learning by search. 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. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762
Prerequisite: Approval of the Academic Head or nominee
683
COMPSCI 761
: Advanced Topics in Artificial Intelligence2023 Semester Two (1235)
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.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 367
Restriction: COMPSCI 367
684
COMPSCI 761
: Advanced Topics in Artificial Intelligence2022 Semester Two (1225)
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.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 367
Restriction: COMPSCI 367
685
COMPSCI 761
: Advanced Topics in Artificial Intelligence2021 Semester Two (1215)
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. Recommended preparation: COMPSCI 220, 225.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 367
Restriction: COMPSCI 367
686
COMPSCI 761
: Advanced Topics in Artificial Intelligence2020 Semester Two (1205)
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. Recommended preparation: COMPSCI 220, 225.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 367
Restriction: COMPSCI 367
687
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
688
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
689
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
690
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
691
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
692
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
693
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
694
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
695
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
696
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
697
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
698
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
699
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
700
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
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