There are following classes each week:
· one 2 hour lecture
· one 1 hour SGTA class
Check timetables.mq.edu.au or the unit iLearn page for class details.
Lectures begin in Week 1. Lecture notes are available on iLearn prior to the lecture.
SGTA classes begin in week 1 and are based on work from the current week’s lecture.
Students should obtain the lecture overheads from iLearn prior to the lecture. The lecture overheads are available module by module.
The following are recommended reading books for this unit:
- Pattern Recognition and Machine Learning, Bishop, Christopher M. 2006.
- Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
- Computational Statistics Handbook with MATLAB®, W. L. Martinez and A. R. Martinez, Chapman & Hall. (QA276.4.M272)
- Local regression and likelihood, C. Loader, Springer-Verlag, 1999. QA276.8 .L6/1999.
- Quantile Regression, Roger Koenker, Cambridge University Press 2005,
Unit webpage is located on iLearn at https://ilearn.mq.edu.au.
You can only access the material on iLearn if you are formally enrolled in the unit. All lecturing materials are available at this webpage.
Teaching and Learning Strategy
The unit is taught in both traditional mode and external mode. In traditional mode, students are on campus in standard semesters with weekly lectures. In external mode, students access all teaching material from iLearn and do not attend lectures on campus.
Students are expected to
· attend all the lectures if enrolled internally;
· have read through the material to be covered using the lecture notes provided on iLearn;
· submit assignments on time via iLearn;
· contact the unit convenors in advance if for any reason, you cannot hand in your assessment tasks on time;
Refer to the next section for a week-by-week list of topics to be covered in this unit.
Software used in teaching
We are using R through Rstudio in teaching this unit. R and Rstudio are free software and are widely used nowadays by statisticians.