Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: Contact Hours: 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week. |
Total Time Commitment:
Estimated Total Time Commitment - 170 hours
Study Period Commencement:
|Recommended Background Knowledge:|| |
Study Period Commencement:
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorDr Guoqi Qian
Computing techniques and data mining methods are indispensible in modern statistical research and applications, where “Big Data” problems are often involved. This subject will introduce a number of recently developed statistical data mining methods that are scalable to large datasets and high-performance computing. These include regularized regression such as the Lasso; tree based methods such as bagging, boosting and random forests; and support vector machines. Important statistical computing algorithms and techniques used in data mining will be explained in detail. These include the bootstrap, cross-validation, the EM algorithm, and Markov chain Monte Carlo methods including the Gibbs sampler and Metropolis-Hastings algorithm.
After completing this subject students should gain:
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
Master of Philosophy - Engineering |
Master of Science (Mathematics and Statistics)
Mathematics and Statistics |
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