- Code MATH6210
- Unit Value 6 units
- Offered by Mathematical Sciences Institute
- ANU College ANU Joint Colleges of Science
- Course subject Mathematics
- Areas of interest Mathematics
- Academic career PGRD
- Mode of delivery In Person
Attention will be given to:
- Generalizability and predictive accuracy, in the practical contexts in which methods are applied.
- Low-dimensional visual representation of results, as an aid to diagnosis and insight.
- Interpretability of model parameters, including potential for misinterpretation.
Topic to be covered include:
- Basic statistical ideas - populations, distributions, samples and random samples
- Classification models and methods - including: linear discriminant analysis; trees; random forests; neural nets; boosting and bagging approaches; support vector machines.
- Linear regression approaches to classification, compared with linear discriminant analysis,
- The training/test approach to assessing accuracy, and cross-validation.
- Strategies in the (common) situation where source and target population differ, typically in time but in other respects also.
- Unsupervised models - kmeans, association rules, hierarchical clustering, model based clusters.
- Low-dimensional views of classification results - distance methods and ordination.
- Strategies for working with large data sets.
- Practical approaches to classification with real life data sets, using different methods to gain different insights into presentation.
- Privacy and security.
- Use of the R system for handling the calculations.
Note: Graduate students attend joint classes with undergraduates but will be assessed separately.
Upon successful completion, students will have the knowledge and skills to:
On satisfying the requirements of this course, students will have the knowledge and skills to:1. Explain the fundamental issues involved in the use of the training/test methodology, cross-validation and the bootstrap to provide accuracy assessments.
2. Understand and explain ideas of source and target sample, and their relevance to the practical application of classification and other data mining techniques.
3. Demonstrate accurate and efficient use of classification and related data mining techniques, using the R system for the computations.
4. Demonstrate capacity for mathematical reasoning through analyzing, proving and explaining concepts from the theory that underpins classification and related data mining methods.
5. Apply problem-solving using classification and related data mining techniques to diverse situations in business, biology, engineering and other sciences.
Assessment will be based on:
- 3 Assignments (60%; LO 1-5)
- Presentation (40%; LO1-5)
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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WorkloadOffered subject to staff availability and student demand. Regular meetings.
Requisite and Incompatibility
You will need to contact the Mathematical Sciences Institute to request a permission code to enrol in this course.
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.