Search Course Outline

Search

Showing 25 course outlines from 10415 matches

2251

COMPSCI 751

: Advanced Topics in 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. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
2252

COMPSCI 751

: Advanced Topics in 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. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
2253

COMPSCI 752

: Big Data Management
2025 Semester One (1253)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 220, 351
Subject: Computer Science
No pre-requisites or restrictions
2254

COMPSCI 752

: Big Data Management
2024 Semester One (1243)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 220, 351
Subject: Computer Science
No pre-requisites or restrictions
2255

COMPSCI 752

: Big Data Management
2023 Semester One (1233)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.
Subject: Computer Science
No pre-requisites or restrictions
2256

COMPSCI 752

: Big Data Management
2022 Semester One (1223)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
2257

COMPSCI 752

: Big Data Management
2021 Semester One (1213)
Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-view), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
2258

COMPSCI 752

: Big Data Management
2020 Semester One (1203)
Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-vie), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
2259

COMPSCI 753

: Algorithms for Massive Data
2025 Semester Two (1255)
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
No pre-requisites or restrictions
2260

COMPSCI 753

: Algorithms for Massive Data
2024 Semester Two (1245)
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
No pre-requisites or restrictions
2261

COMPSCI 753

: Algorithms for Massive Data
2023 Semester Two (1235)
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
2262

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
2263

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
2264

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
2265

COMPSCI 760

: Advanced Topics in Machine Learning
2025 Semester Two (1255)
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
2266

COMPSCI 760

: Advanced Topics in Machine Learning
2025 Semester One (1253)
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
2267

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
2268

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
2269

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
2270

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
2271

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
2272

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
2273

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
2274

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
2275

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