ETC3250/5250 Introduction to Machine Learning

Welcome! Meet the teaching team

Chief examiner: Professor Dianne Cook

Communication: All questions need to be communicated through the Discussion forum. Any of a private matter can be addressed to etc3250.clayton-x@monash.edu or through a private message on the edstem forum. Emails should never be sent directly to tutors or the instructor.

Tutors:

• Patrick: 3rd year PhD student working on computer vision for reading residual plots
• Harriet: 2nd year PhD student working on visualisation of uncertainty
• Jayani: 2nd year PhD student working on methods for understanding how non-linear dimension reduction warps your data
• Krisanat: MBAt graduate, aspiring to be PhD student in 2025

What this course is about

• select and develop appropriate models for clustering, prediction or classification.
• estimate and simulate from a variety of statistical models.
• measure the uncertainty of a prediction or classification using resampling methods.
• apply business analytic tools to produce innovative solutions in finance, marketing, economics and related areas.
• manage very large data sets in a modern software environment.
• explain and interpret the analyses undertaken clearly and effectively.

Assessment

• Weekly learning quizzes: 3% DUE: Mondays 9am
• Assignment 1: 9%
• Assignment 2: 9%
• Assignment 3: 9%
• Project: 10%
• Final exam: 60%

How to do well

• Keep up-to-date with content:
  • participate in the lecture each week
  • attend tutorials
  • complete weekly learning quiz to check your understanding
  • read the relevant sections of the resource material
  • run the code from lectures in the qmd files
• Begin assessments early, when posted, map out a plan to complete it on time
• Ask questions

Framing the problem

1. Supervised classification: categorical \(y_i\) is available for all \(x_i\)
2. Unsupervised learning: \(y_i\) unavailable for all \(x_i\)