This course is available for entry:
- Semester 1 (February)
100 credit points taken over 1 years full-time.
Your course will comprise of:
- Four core subjects in computer science (50 points) and two core subjects in statistics (50 points)
Students admitted into the course will come from a variety of backgrounds, some having no computer science and statistics backgrounds, while other students may have limited background in either or both areas. Students who have already studied some of the core subjects, or their equivalents, may be granted exemption from these subjects. In these cases students will need to take additional subjects to yield 100 points in total, with 50 points coming from each of the two disciplines.
|Algorithms and ComplexityCore||12.5|
Algorithms and Complexity
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in ord...
Detailed Information COMP90038
|Programming and Software DevelopmentCore||12.5|
Programming and Software Development
AIMS The aims for this subject is for students to develop an understanding of approaches to solving moderately complex problems with computers, and to be able to demonstrate proficiency in designing and writing programs. The programming language used is Java. INDICATIVE CONTENT Topics covered will include: Java basics Console input/output Control flow Defining classes Using object references Programming with arrays Inheritance Polymorphism and abstract classes Exception handling UML basics Interfaces Generics.
Detailed Information COMP90041
|Database Systems & Information ModellingCore||12.5|
Database Systems & Information Modelling
AIMS The subject introduces key topics in modern information organization, particularly with regard to structured databases. The well-founded relational theory behind modern structured query language (SQL) engines, has given them as much a place behind the web site of an organization and on the desktop, as they traditionally enjoyed on corporate mainframes. Topics covered may include: the managerial view of data, information and knowledge; conceptual, logical and physical data modelling; normalization and de-normalization; the SQL language; data integrity; transaction processing, data warehousing, web services and organizational memory technologies. This is a core foundation subject for b...
Detailed Information INFO90002
AIMS Much of the world's knowledge is stored in the form of unstructured data (e.g. text) or implicitly in structured data (e.g. databases). In this subject students will learn algorithms and data structures for extracting, retrieving and analysing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, regular expressions, document indexing, text retrieval, clustering, classification and prediction, pattern mining, and approaches to evaluation of knowledge technologies. INDICATIVE CONTENT Introduction to Knowledge Technologies; String search; Genomics; Text processing and search; Web search and retrieval; Introduction...
Detailed Information COMP90049
|Methods of Mathematical StatisticsCore||25|
Methods of Mathematical Statistics
This subject introduces probability and the theory underlying modern statistical inference. Properties of probability are reviewed, univariate and multivariate random variables are introduced, and their properties are developed. It demonstrates that many commonly used statistical procedures arise as applications of a common theory. Both classical and Bayesian statistical methods are developed. Basic statistical concepts including maximum likelihood, sufficiency, unbiased estimation, confidence intervals, hypothesis testing and significance levels are discussed. Computer packages are used for numerical and theoretical calculations.
Detailed Information MAST90105
|A First Course in Statistical LearningCore||25|
A First Course in Statistical Learning
Supervised statistical learning is based on the widely used linear models that model a response as a linear combination of explanatory variables. Initially this subject develops an elegant unified theory for a quantitative response that includes the estimation of model parameters, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Some classification methods for qualitative responses are then developed. This subject then considers computational techniques, including the EM algorithm. Bayes methods and Monte-Carlo methods are considered. The subject concludes by considering some unsupervised learning techniques.
Detailed Information MAST90194