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Showing 25 course outlines from 4473 matches

851

COMPSCI 351

: Fundamentals of Database Systems
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 751, SOFTENG 351
852

COMPSCI 351

: Fundamentals of Database Systems
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 751, SOFTENG 351
853

COMPSCI 351

: Fundamentals of Database Systems
2021 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 751, SOFTENG 351
854

COMPSCI 361

: Machine Learning
2025 Semester One (1253)
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.
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 762
855

COMPSCI 361

: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
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 762
856

COMPSCI 361

: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
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 762
857

COMPSCI 361

: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
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 762
858

COMPSCI 361

: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
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 762
859

COMPSCI 361

: 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. Understand the foundations of machine learning, and introduce practical skills to solve different problems.
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 762
860

COMPSCI 367

: Artificial Intelligence
2025 Semester Two (1255)
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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and COMPSCI 225 or MATHS 254, or SOFTENG 282 and 284
Restriction: COMPSCI 761
861

COMPSCI 367

: Artificial Intelligence
2024 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and COMPSCI 225 or MATHS 254, or SOFTENG 282 and 284
Restriction: COMPSCI 761
862

COMPSCI 367

: Artificial Intelligence
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
Restriction: COMPSCI 761
863

COMPSCI 367

: Artificial Intelligence
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
864

COMPSCI 367

: Artificial Intelligence
2021 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
865

COMPSCI 367

: 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
Restriction: COMPSCI 761
866

COMPSCI 369

: Computational Methods in Interdisciplinary Science
2025 Semester One (1253)
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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
867

COMPSCI 369

: Computational Methods in Interdisciplinary Science
2024 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
868

COMPSCI 369

: Computational Methods in Interdisciplinary Science
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 220, and COMPSCI 225 or MATHS 254
869

COMPSCI 369

: Computational Methods in Interdisciplinary Science
2022 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.
Subject: Computer Science
Prerequisite: COMPSCI 220 and 15 points from COMPSCI 225, MATHS 254, 255
870

COMPSCI 373

: Computer Graphics and Image Processing
2025 Semester One (1253)
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.
Subject: Computer Science
Prerequisite: COMPSCI 210, 230, or COMPSYS 201 and SOFTENG 281
Restriction: COMPSCI 771
871

COMPSCI 373

: Computer Graphics and Image Processing
2024 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.
Subject: Computer Science
Prerequisite: COMPSCI 210, 230, or COMPSYS 201 and SOFTENG 281
Restriction: COMPSCI 771
872

COMPSCI 373

: Computer Graphics and Image Processing
2023 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.
Subject: Computer Science
Prerequisite: COMPSCI 210, 230
Restriction: COMPSCI 771
873

COMPSCI 373

: Computer Graphics and Image Processing
2022 Semester One (1223)
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.
Subject: Computer Science
Prerequisite: COMPSCI 210, 230
Restriction: COMPSCI 771
874

COMPSCI 373

: Computer Graphics and Image Processing
2021 Semester One (1213)
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.
Subject: Computer Science
Prerequisite: COMPSCI 210, 230
Restriction: COMPSCI 771
875

COMPSCI 380

: Project in Computer Science
2025 Semester Two (1255)
Each student taking one of these courses will be expected to do an individual practical project under the supervision of a member of staff. Only students with excellent academic records will be allowed to take these courses, and only after a supervisor and topic have been agreed upon by the Head of Department.
Subject: Computer Science
Prerequisite: Approval of Academic Head or nominee
Restriction: COMPSCI 690 To complete this course students must enrol in COMPSCI 380 A and B, or COMPSCI 380