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Showing 25 course outlines from 3694 matches
701
COMPSCI 345
: Human-computer Interaction2021 Semester One (1213)
Human behaviour and humans' expectations of computers. Computer interfaces and the interaction between humans and computers. The significance of the user interface, interface design and user centred design process in software development. Interface usability evaluation methodologies and practice. Includes a group development and evaluation project using current implementation techniques and tools.
Prerequisite: COMPSCI 230 or SOFTENG 206
Restriction: SOFTENG 350
Restriction: SOFTENG 350
702
COMPSCI 345
: Human-computer Interaction2020 Semester One (1203)
Human behaviour and humans' expectations of computers. Computer interfaces and the interaction between humans and computers. The significance of the user interface, interface design and user centred design process in software development. Interface usability evaluation methodologies and practice. Includes a group development and evaluation project using current implementation techniques and tools.
Prerequisite: COMPSCI 230 or SOFTENG 206
Restriction: SOFTENG 350
Restriction: SOFTENG 350
703
COMPSCI 350
: Mathematical Foundations of Computer Science2024 Semester One (1243)
The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages (finite automata, context-free grammars, Turing and register machines) are studied. Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed.
Prerequisite: COMPSCI 220 or PHIL 222, and COMPSCI 225 or MATHS 254
704
COMPSCI 350
: Mathematical Foundations of Computer Science2023 Semester One (1233)
The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages (finite automata, context-free grammars, Turing and register machines) are studied. Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed.
Prerequisite: COMPSCI 220 or PHIL 222, and COMPSCI 225 or MATHS 254
705
COMPSCI 350
: Mathematical Foundations of Computer Science2022 Semester One (1223)
The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages (finite automata, context-free grammars, Turing and register machines) are studied. Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed.
Prerequisite: COMPSCI 220 or PHIL 222, and 15 points from COMPSCI 225, MATHS 254, 255
706
COMPSCI 350
: Mathematical Foundations of Computer Science2020 Semester One (1203)
The aim of this course is to present mathematical models for programming languages and computation, and derive some theorems regarding what can and cannot be computed. Abstract programming languages (finite automata, context-free grammars, Turing and register machines) are studied. Basic concepts for programming languages, limits on computational power and algorithmic complexity are presented. Church-Turing thesis and quantum computing are briefly and critically discussed.
Prerequisite: COMPSCI 220 or PHIL 222, and 15 points from COMPSCI 225, MATHS 254, 255
707
COMPSCI 351
: Fundamentals of Database Systems2024 Semester One (1243)
Database principles. Relational model, Relational Algebra, Relational Calculus, SQL, SQL and Programming Languages, Entity Relationship Model, Normalisation, Query Processing and Query Optimisation, ACID Transactions, Transaction Isolation Levels, Database Recovery, Database Security, Databases and XML.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 751, SOFTENG 351
Restriction: COMPSCI 751, SOFTENG 351
708
COMPSCI 351
: Fundamentals of Database Systems2023 Semester One (1233)
Database principles. Relational model, Relational Algebra, Relational Calculus, SQL, SQL and Programming Languages, Entity Relationship Model, Normalisation, Query Processing and Query Optimisation, ACID Transactions, Transaction Isolation Levels, Database Recovery, Database Security, Databases and XML.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 751, SOFTENG 351
Restriction: COMPSCI 751, SOFTENG 351
709
COMPSCI 351
: Fundamentals of Database Systems2022 Semester One (1223)
Database principles. Relational model, Relational Algebra, Relational Calculus, SQL, SQL and Programming Languages, Entity Relationship Model, Normalisation, Query Processing and Query Optimisation, ACID Transactions, Transaction Isolation Levels, Database Recovery, Database Security, Databases and XML.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 751, SOFTENG 351
Restriction: COMPSCI 751, SOFTENG 351
710
COMPSCI 351
: Fundamentals of Database Systems2021 Semester One (1213)
Database principles. Relational model, Relational Algebra, Relational Calculus, SQL, SQL and Programming Languages, Entity Relationship Model, Normalisation, Query Processing and Query Optimisation, ACID Transactions, Transaction Isolation Levels, Database Recovery, Database Security, Databases and XML.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 751, SOFTENG 351
Restriction: COMPSCI 751, SOFTENG 351
711
COMPSCI 361
: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 762
Restriction: COMPSCI 762
712
COMPSCI 361
: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 762
Restriction: COMPSCI 762
713
COMPSCI 361
: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 762
Restriction: COMPSCI 762
714
COMPSCI 361
: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 762
Restriction: COMPSCI 762
715
COMPSCI 361
: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 762
Restriction: COMPSCI 762
716
COMPSCI 367
: Artificial Intelligence2024 Semester Two (1245)
Covers algorithms and representational schemes used in artificial intelligence. AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks. Tasks such as game playing (adversarial search), planning, and natural language processing. Discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.
Prerequisite: COMPSCI 220 and COMPSCI 225 or MATHS 254, or SOFTENG 282 and 284
Restriction: COMPSCI 761
Restriction: COMPSCI 761
717
COMPSCI 367
: Artificial Intelligence2023 Semester Two (1235)
Covers algorithms and representational schemes used in artificial intelligence. AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks. Tasks such as game playing (adversarial search), planning, and natural language processing. Discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 761
Restriction: COMPSCI 761
718
COMPSCI 367
: Artificial Intelligence2022 Semester Two (1225)
Covers algorithms and representational schemes used in artificial intelligence. AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks. Tasks such as game playing (adversarial search), planning, and natural language processing. Discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
Restriction: COMPSCI 761
719
COMPSCI 367
: Artificial Intelligence2021 Semester Two (1215)
Covers algorithms and representational schemes used in artificial intelligence. AI search techniques (e.g., heuristic search, constraint satisfaction, etc.) for solving both optimal and satisficing tasks. Tasks such as game playing (adversarial search), planning, and natural language processing. Discusses and examines the history and future of AI and the ethics surrounding the use of AI in society.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
Restriction: COMPSCI 761
720
COMPSCI 367
: 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.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
Restriction: COMPSCI 761
721
COMPSCI 369
: Computational Methods in Interdisciplinary Science2024 Semester One (1243)
Many sciences use computational methods that involve the development and application of computer algorithms and software to answer scientific questions. This course looks at how to tackle these interdisciplinary problems through methods like probabilistic computer modelling, computer-based statistical inference, and computer simulations. The material is largely motivated by the life sciences but also uses examples from other sciences. It focuses on modelling and analysing real-world data with an emphasis on analysing DNA sequence data. No background in physical or life sciences is assumed.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
722
COMPSCI 369
: Computational Methods in Interdisciplinary Science2023 Semester One (1233)
Many sciences use computational methods that involve the development and application of computer algorithms and software to answer scientific questions. This course looks at how to tackle these interdisciplinary problems through methods like probabilistic computer modelling, computer-based statistical inference, and computer simulations. The material is largely motivated by the life sciences but also uses examples from other sciences. It focuses on modelling and analysing real-world data with an emphasis on analysing DNA sequence data. No background in physical or life sciences is assumed.
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
723
COMPSCI 369
: Computational Methods in Interdisciplinary Science2022 Semester One (1223)
Many sciences use computational methods that involve the development and application of computer algorithms and software to answer scientific questions. This course looks at how to tackle these interdisciplinary problems through methods like probabilistic computer modelling, computer-based statistical inference, and computer simulations. The material is largely motivated by the life sciences but also uses examples from other sciences. It focuses on modelling and analysing real-world data with an emphasis on analysing DNA sequence data. No background in physical or life sciences is assumed.
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
724
COMPSCI 373
: Computer Graphics and Image Processing2024 Semester One (1243)
Basic geometric processes including transformations; viewing and projection; back projection and ray tracing. Graphics modelling concepts: primitives, surfaces, and scene graphs, lighting and shading, texture mapping, and curve and surface design. Graphics and image processing fundamentals: image definition and representation, perception and colour models, grey level and colour enhancement, neighbourhood operations and filtering. Use of the OpenGL graphics pipeline.
Prerequisite: COMPSCI 210, 230, or COMPSYS 201 and SOFTENG 281
Restriction: COMPSCI 771
Restriction: COMPSCI 771
725
COMPSCI 373
: Computer Graphics and Image Processing2023 Semester One (1233)
Basic geometric processes including transformations; viewing and projection; back projection and ray tracing. Graphics modelling concepts: primitives, surfaces, and scene graphs, lighting and shading, texture mapping, and curve and surface design. Graphics and image processing fundamentals: image definition and representation, perception and colour models, grey level and colour enhancement, neighbourhood operations and filtering. Use of the OpenGL graphics pipeline.
Prerequisite: COMPSCI 210, 230
Restriction: COMPSCI 771
Restriction: COMPSCI 771
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