Course Title: Machine Learning

Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2319

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017

Course Coordinator: Dr Vural Aksakalli

Course Coordinator Phone: +61 3 9925 2277

Course Coordinator Email: vural.aksakalli@rmit.edu.au

Course Coordinator Location: 008.09.084

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324 - Introduction to Statistics or Equivalent

MATH2267 - Essential Mathematics for Analytics or Equivalent

Basic knowledge of the R statistical programming language


Course Description

This course will cover

  1. Data preparation for machine learning
  2. Measuring model fit and the bias-variance trade-off
  3. Nearest neighbours, decision trees, logistic regression
  4. Regularization: ridge and lasso regression
  5. Naïve Bayes, support vector vector machines
  6. Ensemble methods: bagging, boosting, and stacking
  7. Clustering and association analysis

 

The course will be delivered using the statistical programming language R.


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Teamwork and project management

  • the ability to constructively engage with other team members and resolve conflict.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.


On completion of this course you will be able to:

  1. Understand the fundamental concepts of machine learning, the underlying assumptions, and its limitations.
  2. Develop a thorough understanding of popular classification and prediction algorithms as well as common unsupervised machine learning techniques.
  3. Perform efficient implementation of these techniques on real data using the R programming language.
  4. Assess and compare performance of different methods for a given machine learning problem


Overview of Learning Activities

The course will be delivered through a combination of face-to-face lectures and computer practice. While attendance at weekly lectures is beneficial, there is an expectation that you will spend more time out of class on this course, in particular on the course project. The course will be supported by the Blackboard learning management system.

 

Total study hours

Your will undertake 3 hours face-to-face learning every week through lecture/ lab sessions. It is expected that you will commit a minimum of 4-6 hours per week out-of-class in independent & collaborative study.


Overview of Learning Resources

A list of recommended textbooks for this course will be provided on Blackboard. All course materials will be posted on Blackboard.

The software packages can be accessed from the school computer labs, as well as through the RMIT MyDesktop system anywhere anytime.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats  


Overview of Assessment

This course has no hurdle requirements.

 

Assessment tasks

 

Assessment Task 1: Project

Weighting 20%

This assessment task supports CLOs 1, 2, 3 & 4

Assessment 2: Mid-semester Test

Weighting 30%

This assessment task supports CLOs 1, 2 & 4.

Assessment 3: Final Exam

Weighting 50% 

This assessment supports CLOs 1, 2 & 4.