# Search Course Outline

### Showing 25 course outlines from 3697 matches

851

#### COMPSCI 753

: Algorithms for Massive Data2021 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

Prerequisite: Approval of the Academic Head or nominee

852

#### COMPSCI 753

: Uncertainty in Data2020 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

Prerequisite: Approval of the Academic Head or nominee

853

#### COMPSCI 760

: Advanced Topics in Machine Learning2024 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.

Prerequisite: COMPSCI 361 or 762

854

#### COMPSCI 760

: Advanced Topics in Machine Learning2024 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.

Prerequisite: COMPSCI 361 or 762

855

#### 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

856

#### COMPSCI 760

: Advanced Topics in Machine Learning2023 Semester One (1233)

Prerequisite: COMPSCI 361 or 762

857

#### 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

858

#### 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

859

#### 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

860

#### 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

861

#### COMPSCI 760

: Datamining and Machine Learning2020 Semester One (1203)

Prerequisite: Approval of the Academic Head or nominee

862

#### COMPSCI 761

: Advanced Topics in Artificial Intelligence2024 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.

Prerequisite: COMPSCI 220 and 225, or COMPSCI 220 and MATHS 254, or COMPSCI 713 and 714, or COMPSCI 718

Restriction: COMPSCI 367

Restriction: COMPSCI 367

863

#### 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

864

#### 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

865

#### 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

866

#### 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

867

#### COMPSCI 762

: Foundations of Machine Learning2024 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.

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

Restriction: COMPSCI 361

868

#### 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

869

#### 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

870

#### 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

871

#### 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

872

#### 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

873

#### 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

874

#### 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

875

#### 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

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