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Showing 25 course outlines from 2938 matches

676

COMPSCI 760

: Advanced Topics in Machine Learning
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762
677

COMPSCI 760

: Advanced Topics in Machine Learning
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762
678

COMPSCI 760

: Machine Learning
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762
679

COMPSCI 760

: Datamining and Machine Learning
2021 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
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
680

COMPSCI 760

: Datamining and Machine Learning
2021 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
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
681

COMPSCI 760

: Datamining and Machine Learning
2020 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
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
682

COMPSCI 760

: Datamining and Machine Learning
2020 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
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
683

COMPSCI 761

: Advanced Topics in Artificial Intelligence
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 367
684

COMPSCI 761

: Advanced Topics in Artificial Intelligence
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 367
685

COMPSCI 761

: Advanced Topics in Artificial Intelligence
2021 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.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 367
686

COMPSCI 761

: Advanced Topics in Artificial Intelligence
2020 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.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 367
687

COMPSCI 762

: Foundations of Machine Learning
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 or 717, and 15 points from DATASCI 100, STATS 101, 108, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 361
688

COMPSCI 762

: Foundations of Machine Learning
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 361
689

COMPSCI 762

: Advanced Machine Learning
2021 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
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 361
690

COMPSCI 762

: Advanced Machine Learning
2020 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
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 361
691

COMPSCI 765

: Modelling Minds
2023 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.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
692

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
693

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
694

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
695

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
696

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
697

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
698

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
699

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
700

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