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Showing 25 course outlines from 3702 matches

851

COMPSCI 753

: Algorithms for Massive Data
2022 Semester Two (1225)
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
852

COMPSCI 753

: Algorithms for Massive Data
2021 Semester Two (1215)
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
853

COMPSCI 753

: Uncertainty in Data
2020 Semester Two (1205)
Modern applications such as electronic commerce, social networks, and location services are expecting efficient big data solutions. This course exposes practitioners to challenging problems in managing and mining big data. It introduces a wide spectrum of advanced techniques that underpin big data processing. Best-practices and current developments in big data research are also explored. Recommended preparation: COMPSCI 351
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
854

COMPSCI 760

: Advanced Topics in Machine Learning
2024 Semester Two (1245)
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
855

COMPSCI 760

: Advanced Topics in Machine Learning
2024 Semester One (1243)
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
856

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
857

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
858

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
859

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
860

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
861

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
862

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
863

COMPSCI 761

: Advanced Topics in Artificial Intelligence
2024 Semester Two (1245)
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 225, or COMPSCI 220 and MATHS 254, or COMPSCI 713 and 714, or COMPSCI 718
Restriction: COMPSCI 367
864

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
865

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
866

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
867

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
868

COMPSCI 762

: Foundations of Machine Learning
2024 Semester One (1243)
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 713 and 714, or COMPSCI 718, or 15 points from DATASCI 100, STATS 101, 108 and COMPSCI 220 or 717 and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 361
869

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
870

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
871

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
872

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
873

COMPSCI 764

: Deep Learning
2024 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.
Subject: Computer Science
Prerequisite: COMPSCI 361 or 762, or COMPSCI 713 and 714
874

COMPSCI 765

: Modelling Minds
2024 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
Subject: Computer Science
No pre-requisites or restrictions
875

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