Courses

Home | News | Jobs | Research Topics | People | Projects | Publications | Resources | Blogs | Students | Seminars | Visiting | Contact 

 

This page gives a summary of current undergraduate and graduate courses our researchers teach or contribute to which support the student of machine learning and related fields.

 

Current Undergraduate Machine Learning courses

Introduction to Statistical Machine Learning 2013 COMP4670 (ANU)

Statistical Machine Learning plays a key role in science and technology. Some examples of applications using Statistical Machine Learning techniques are e-mail spam filtering,web page ranking, handwritten ZIP code recognition,identification of risk factors for cancer, object recognition in computer vision, and, autonomous robot navigation.
This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

For more information about this course, click here.

Artificial Intelligence COMP3620 (ANU)

Artificial intelligence is the science that studies and develops methods of making computers more /intelligent/. The focus of this course is on core AI techniques for knowledge representation, search, reasoning, learning and designing intelligent agents. The course also aims to give an overview of other topics within AI, such as for example robotics, and of the historical, philosophical, and logical foundations of AI.

For more information about this course, click here.

Information Theory COMP2610 (ANU)

Information theory studies the fundamental limits of the representation and transmission of information. This course provides an introduction to information theory, studying fundamental concepts such as probability, information, and entropy and examining their applications in the areas of data compression, coding, communications, pattern recognition and probabilistic inference.

For more information about this course, click here.

Document Analysis COMP4650 (ANU)

Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the document" and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.

For more information about this course, click here.

Advanced Databases and Data Mining COMP3420 (ANU)

This course examines the design of databases and data warehouses and their use for data mining; and investigates associated issues. Topics may include: relational theory and conceptual modelling; privacy and security; statistical databases; distributed databases; data warehousing; data cleaning and integration; and data mining concepts and techniques.

For more information about this course, click here.


Current Postgraduate Machine Learning courses

Introduction to Statistical Machine Learning COMP6467 (ANU)

This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modelling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

For more information about this course, click here.

Document Analysis COMP6490 (ANU)

Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the document" and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.

For more information about this course, click here.

Advanced Topics in Statistical Machine Learning COMP8650 (ANU)

This course explores a selected area relevant to statistical machine learning in depth, and will be taught by an SML staff member of internationally recognised standing and research interest in that area. Based on current SML staffing, this will be one of:

•kernel methods
•graphical models
•reinforcement learning
•convex analysis
•optimisation
•bioinformatics
•minimal description length principle
•topics in information theory
•decision theory

For more information about this course, click here.

Algorithms and Techniques for Data Mining COMP8400 (ANU)

Large amounts of data are increasingly being collected by public and private organisations, and research projects. Additionally, the Internet provides a very large source of information about almost every aspect of human life and society.

This course provided a practical focus on the technology and research in the area. It focuses on the algorithms and techniques and less on the mathematical and statistical foundations.

For more information about this course, click here.

Robot Software Architectures COMP3431/9431 (UNSW)

This course gives both a theoretical and a practical introduction to the control of robotic systems. The course is in two parts:
Practical Robotics: This part of the course gives students a practical introduction to some specific robot architectures; the main system architecture will be the Robot Operating System.

An overview of agent planning and learning systems: This part of the course gives students an introduction to the theory of decision making: How can an agent build up knowledge of its world? Having done that, how can an agent choose its actions? A variety of different decision making and planning architectures are covered; including classical planning, reinforcement learning and cognitive architectures.

For more information about this course, click here.

Knowledge Technologies COMP30018 (UoM)

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 storing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, clustering, regular expressions, pattern mining, Bayesian learning, instance-based learning, document indexing, database storage and indexing, and text retrieval.

For more information about this course, click here.

Statistical and Evolutionary Learning COMP90051 (UoM)

With exponential increases in the amount of data becoming available in fields such as finance and biology, and on the web, there is an ever-greater need for methods to detect interesting patterns in that data, and classify novel data points based on curated data sets. Statistical machine learning and evolutionary computation provide the means to perform this analysis automatically, and in doing so to enhance understanding of general processes or to predict future events.
Topics covered will include: association rules, clustering, instance-based learning, statistical learning, evolutionary algorithms, swarm intelligence, neural networks, numeric prediction, weakly supervised classification, discretisation, feature selection and classifier combination.

For more information about this course, click here.

Knowledge Discovery and Data Mining COMP5318 (The University of Sydney)

This course offers a comprehensive coverage of well known Data Mining topics including classification, clustering and association rules. A number of specific algorithms and techniques under each category will be discussed. Methods for feature selection, dimensionality reduction and performance evaluation will also be covered. Students will learn and work with appropriate software tools and packages in the laboratory. They will be exposed to relevant Data Mining research.

 For more information about this course, click here.