Search Course Outline
Showing 25 course outlines from 794 matches
176
COMPSCI 719
: Programming with Web Technologies2025 Semester Two (1255)
An examination of developing web-based applications. Client-side technologies: HTML, CSS and Javascript. Server-side technologies to support dynamic Web pages and data access. Fundamental relational database concepts and design techniques. Principles of Web-application design. HCI considerations and mobile clients. Students will build a Web-based application that dynamically generates content involving relational database access.
No pre-requisites or restrictions
177
COMPSCI 719
: Programming with Web Technologies2025 Semester One (1253)
An examination of developing web-based applications. Client-side technologies: HTML, CSS and Javascript. Server-side technologies to support dynamic Web pages and data access. Fundamental relational database concepts and design techniques. Principles of Web-application design. HCI considerations and mobile clients. Students will build a Web-based application that dynamically generates content involving relational database access.
No pre-requisites or restrictions
178
COMPSCI 720
: Advanced Design and Analysis of Algorithms2025 Semester One (1253)
Selected advanced topics in design and analysis of algorithms, such as: combinatorial enumeration algorithms; advanced graph algorithms; analytic and probabilistic methods in the analysis of algorithms; randomised algorithms; methods for attacking NP-hard problems. Recommended preparation: COMPSCI 320
No pre-requisites or restrictions
179
COMPSCI 721
: Randomised Algorithms and Probabilistic Methods2025 Semester One (1253)
Randomised algorithms are algorithms that “flip coins” to make decisions. In many cases, such algorithms are faster, simpler, or more elegant than the classical, deterministic ones. Covers basic principles and techniques used to design and analyse randomised algorithms, and applications of randomised methods in mathematics and computer science. Recommended preparation: STATS 125, COMPSCI 225 or MATHS 254, COMPSCI 320
No pre-requisites or restrictions
180
COMPSCI 725
: Usable Security and Privacy Engineering2025 Semester Two (1255)
The human aspect of cyber security and privacy engineering is relevant to commercial solution development and cyber security and privacy research. Sample topics: secure systems design; usable security systems evaluation; privacy-preserving software systems; threat modelling; economics of usable security and privacy; OWASP Top 10 vulnerabilities. Recommended preparation: 30 points from COMPSCI 313, 314, 320, 335, 340, 351, 702, 734, 742
No pre-requisites or restrictions
181
COMPSCI 726
: Network Defence and Countermeasures2025 Semester Two (1255)
Focuses on the use and deployment of protective systems used in securing internal and external networks. Examines in detail the widely used protocols including SSL, IPSec, DNSSEC as well as covers infrastructure platform protocols including wireless security (IEEE 802.11). Explores current research and developments in the area of network defence and countermeasures. Recommended preparation: COMPSCI 314, 315
No pre-requisites or restrictions
182
COMPSCI 727
: Cryptographic Management2025 Semester One (1253)
Builds on best practices, and compliance standards to establish an advanced understanding of modern cryptographic systems used in securing communications and data storage. Advanced knowledge in modern cryptography management issues such as algorithm selection, generation, distribution, and revocation of encryption keys are applied through a research-based report and a group project. Recommended preparation: COMPSCI 210 or MATHS 120
No pre-requisites or restrictions
183
COMPSCI 732
: Software Tools and Techniques2025 Semester One (1253)
An advanced course examining research issues related to tools and techniques for software design and development. Topics include: techniques for data mapping and data integration, software architectures for developing software tools, issues in advanced database systems. Recommended preparation: COMPSCI 331 or SOFTENG 325 or COMPSCI 718 and 719
Restriction: SOFTENG 750
184
COMPSCI 747
: Computing Education2025 Semester One (1253)
An overview of topics related to the use of technology in education and how people learn computer science concepts. Topics include research methodologies used in computer science education, how novices learn to program, and how technology can engage students in active learning, facilitate collaboration and enhance traditional educational practice. Recommended preparation: 30 points at Stage III in Computer Science or COMPSCI 718
No pre-requisites or restrictions
185
COMPSCI 750
: Computational Complexity2025 Semester Two (1255)
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350
No pre-requisites or restrictions
186
COMPSCI 751
: Advanced Topics in Database Systems2025 Semester One (1253)
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 or COMPSCI 718
Restriction: COMPSCI 351, SOFTENG 351
187
COMPSCI 752
: Big Data Management2025 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
No pre-requisites or restrictions
188
COMPSCI 753
: Algorithms for Massive Data2025 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
No pre-requisites or restrictions
189
COMPSCI 760
: Advanced Topics in Machine Learning2025 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.
Prerequisite: COMPSCI 361 or 762
190
COMPSCI 760
: Advanced Topics in Machine Learning2025 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.
Prerequisite: COMPSCI 361 or 762
191
COMPSCI 761
: Advanced Topics in Artificial Intelligence2025 Semester Two (1255)
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
192
COMPSCI 762
: Foundations of Machine Learning2025 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. 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
193
COMPSCI 764
: Deep Learning2025 Semester Two (1255)
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 713, 714
194
COMPSCI 765
: Modelling Minds2025 Semester One (1253)
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
195
COMPSCI 767
: Intelligent Software Agents2025 Semester One (1253)
An introduction to the design, implementation and use of intelligent software agents (e.g., knowbots, softbots etc). Reviews standard artificial intelligence problem-solving paradigms (e.g., planning and expert systems) and knowledge representation formalisms (e.g., logic and semantic nets). Surveys agent architectures and multi-agent frameworks.
Prerequisite: COMPSCI 367 or 761, or COMPSCI 713 and 714
196
COMPSCI 769
: Natural Language Processing2025 Semester Two (1255)
Examines the progress in enabling AI systems to use natural language for communication and knowledge storage. Explores knowledge formalisation, storage, multiple knowledge systems, theory formation, and the roles and risks of belief, explanation, and argumentation in AI. Includes a significant individual research project.
Prerequisite: COMPSCI 713, 714
197
COMPSCI 773
: Intelligent Vision Systems2025 Semester One (1253)
Computational methods and techniques for computer vision are applied to real-world problems such as 2/3D face biometrics, autonomous navigation, and vision-guided robotics based on 3D scene description. A particular feature of the course work is the emphasis on complete system design. Recommended preparation: COMPSCI 373 and 15 points at Stage II in Mathematics
No pre-requisites or restrictions
198
DATASCI 100
: Data Science for Everyone2025 Semester Two (1255)
Explores how to use data to make decisions through the use of visualisation, programming/coding, data manipulation, and modelling approaches. Students will develop conceptual understanding of data science through active participation in problems using modern data, hands-on activities, group work and projects. DATASCI 100 will help students to build strong foundations in the science of learning from data and to develop confidence with integrating statistical and computational thinking.
No pre-requisites or restrictions
199
DATASCI 709
: Data Management2025 Semester One (1253)
Data management is the practice of collecting, preparing, organising, storing, and processing data so it can be analysed for business decisions. The course will use R and SQL to illustrate the process of data management. This will include principles and best practice in data wrangling, visualisation, modelling, querying, and updating.
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: COMPSCI 351, 751, STATS 383, 707, 765
Restriction: COMPSCI 351, 751, STATS 383, 707, 765
200
DATASCI 779
: Statistical Computing Skills for Professional Data Scientists2025 Semester One (1253)
Fundamental topics taught in statistical computing and data management including use of data analytic software such as Excel and R for data analysis, programming, graphics, cleaning and manipulating data, use of regular expressions, mark-up languages LaTeX, and R Markdown, use of SQL and DBMSs, reproducible research and symbolic computation. Students will undertake assigned individual research projects to be presented in-class.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
Restriction: STATS 779
Restriction: STATS 779