Requirements to Pass this Unit

• Achieve a total mark equal to or greater than 50%

 

General Faculty Policy on assessment submission deadlines and late submissions: .

• For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance as- sessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
• Should these assessments be missed due to illness or misadventure, students should apply for Special Consideration.
• Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of 0 will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.

Assessments where Late Submissions will be accepted

• Assignment 1 - Yes, Standard Late Penalty applies
• Assignment 2 - Yes, Standard Late Penalty applies
• Assignment 3 - Yes, Standard Late Penalty applies

Special Consideration Policy: 

The Special Consideration Policy aims to support students who have been impacted by short- term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the written assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.

You should attend the 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. 

 

Prescribed texts

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

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. 

Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to your lecturers from your university email address.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coron- avirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.

 

Week Topic

Weeek	Lectures	Topics	SGTA	Task Due
1	Statistics and Machine Learning: Estimation Methods (Part 1)	- Linear Model - Loss function  - Maximum Likelihood - Shallow Neural network
2	Statistics and Machine Learning: Estimation Methods (Part 2)	- Logistic Model - Cross entropy - Shallow Neural network - Multi-classification	Maximum Likelihood optimization Gradient Descent
3	Optimisation procedures	- Convexity  - Gradient Descent  - Stochastic Gradient Descent	"One versus all" MNIST Data set
4	Beyond Linearity and overfitting	- polynomial model - one-layer NN - Train/validation/Test - Cross validation	Implementation: GD, SGD, Mini-Batch	Assignment 1 due
5	Penalized Regression	- Colinearity -Ridge regression -Lasso model	Investigation overfitting - polynomial regression - Non linear boundary classification
6	EM Algorithm	- missing data - Mixture Model	Implementation: ridge regression  use software R package for Lasso
7	Monte-Carlo method for hypothesis testing		Implementation EM for Mixture model: - Gaussian case
	Session Break
8	Bootstrapping method for Confidence Interval			Assignment 2 due
9	Histogram & density estimation
10	Kernel density estimation
11	Kernel regression
12	Quantile regression
13	Revision			Assignment 3 due

Students should read the lecture notes, which will be available at the unit web page, before the lecture.