Introduction to Machine Learning Techniques: Online - (2 days)

This course is designed as an applied introduction to Machine Learning (ML) techniques, with exercises in R and Python to run various ML algorithms.



This course will be offered online via Zoom And will run to the following timetable:

  • 10.00am - 11.30am: Instructional session
  • 12.30pm - 2.00pm: Instructional session
  • 3.00pm - 4.30pm:  Exercises and discussion


Please note: Courses will run on Australian Eastern Daylight Time (GMT +11)



Master Class - runs over 2 days

Dr Joanna Dipnall is a biostatistician with the School of Public Health and Preventative Medicine (SPHPM) at Monash University and Honorary Research Fellow with School of Medicine at Deakin University. She holds a B.Ec(Honours) from Monash University, and a PhD from the School of Medicine at Deakin University. She also lectures and tutors with the Department of Statistics, Data Science and Epidemiology at Swinburne University. Joanna has developed a novel Risk Index for Depression (RID) utilising SEM and machine learning techniques that brought together five key determinants of depression. She has been a teacher of Stata software for over 15 years, training across Australia and overseas and was a member of the Scientific Committee for the Oceania Stata Users Group Meeting in 2017.

Course dates: Friday 5 November 2021 - Saturday 6 November 2021
Course status: Course completed (no new applicants)
Week 1
About this course: 

Machine Learning techniques are becoming increasingly popular across areas of research from computer science to various disciplines of medicine. This branch of artificial intelligence relates to algorithms that learn from data based on specific tasks and performance measures. This course is an introductory applied course, with exercises in R and Python to run various ML algorithms.

Classification, prediction and model selection issues will be discussed. Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspect of the course.

This course primarily focusses on the application of specific ML techniques rather than the complex mathematics behind the ML algorithms and discussion of some of the uses in ML techniques in publications will be discussed at the end of the course.


Course syllabus: 

This course is broken up into the following sections:

Part I: Fundamentals of Machine Learning

Part II: Machine Learning Techniques and Work Flow

Part III: Decision Trees & Random Forests

Part IV: Boosted regression

Part V: Support Vector Machines

Part VI: Machine Learning Techniques in Publications


Participants will be given time to do some ML exercises on their own to practise what they have learned. Exercises and solutions will be provided in both R and Python software.


Course format: 

This workshop will take place online using Zoom.

You will need your own computer with Python and/or R installed,  and an internet connection.

A second screen/monitor is recommended.

Recommended Background: 

This course assumes that participants have:


(1) Sound familiarity with at least one of the two software packages R and/or Python.

(2) sufficient understanding of statistics to be able to comprehend the material covered in the course outline, such as a basic grounding in multiple regression (e.g., linear, logistic, Poisson) and clustering techniques (e.g. Principal components analysis, k-means clustering)

(3) access to either R and/or Python

(4) some experience in using Microsoft Word and Excel or their equivalent

(5) experience using a text editor such as Notepad.



Recommended Texts: 

Course notes will be supplied. Please include a shipping address when you enrol. Your notes will be express posted to this address.


No specific references are suggested but a number will be supplied with the notes handed out for the course.

Course fees
Non Member: 
Full time student Member: