ETC3250/5250

Introduction to Machine Learning

Information

Lecturer: Emi Tanaka

Department of Econometrics and Business Statistics

• emi.tanaka@monash.edu
• Week 0

👩🏻‍🏫 ETC3250/5250 Teaching Team

Dr. Emi Tanaka

Lecturer & Chief Examiner

Contact

• For private matters, contact emi.tanaka@monash.edu using your Monash student email and citing the unit name.
• For other matters on the Moodle discussion board.

Weihao (Patrick) Li

Head Tutor

Harriet Mason

Tutor

Huey (Joyn) Mah

Tutor

Jayani Lakshika Piyadi Gamage

Tutor

Xinrui (Rachel) Wang

Tutor

🎯 ETC3250/5250 Overview

Business analytics involves uncovering the hidden information in masses of business data using statistical graphics, models and algorithms. The most widely used prediction and classification models will be covered. Practical skills in applying techniques to different problems will be developed using a suitable software environment that involves doing reproducible analyses. Topics to be covered include dimension reduction with methods such as principal component analysis, supervised learning with methods such as linear models, discriminant analysis, decision trees and forests, support vector machines, neural networks, and unsupervised methods such as k-means clustering. Techniques for numerical optimisation, Monte Carlo simulation, and resampling methods including bootstrap, cross-validation, and bagging will be discussed. Modelling will include nonlinear relationships and nonparametric methods.

🎯 ETC3250/5250 Learning Objectives

Learning objectives

1. Select and develop appropriate models for clustering, prediction or classification
2. Estimate and simulate from a variety of statistical models
3. Measure the uncertainty of a prediction or classification using resampling methods
4. Apply business analytic tools to produce innovative solutions in finance, marketing, economics and related areas
5. Manage very large data sets in a modern software environment
6. Explain and interpret the analyses undertaken clearly and effectively

🏛️ ETC3250/5250 Materials & Expectations

• 2 hour lectures
• 1.5 hour tutorial
  • only go to the one you are assigned to!
  • bring your laptops for in-person tutorials
• All materials (including link to lecture slides, tutorials, recorded lecture videos and assessment instructions will be available) on Moodle.
• Tutorial solutions will be provided after the tutorial.
• Attend lectures, assigned tutorials and consultation hours as needed.
• Minimum total expected workload is 144 hours, that’s 12 hours each week or 8.5 hours of self study per week.

✋ Consultation Hours

• A total of 7.75 hours of consultation each week.

• See Moodle announcement for the Zoom links or location for the consultations.

• Seek help early and often! We are here to help!

• For coding issues, check out this guide to make it easier for others to help you.

💻 Programming with R

• In this unit, we use the statistical software R.
• You are expected to know already the basics of R as part of the prerequisite of this unit.
• If you need to brush up on your R skills, check out https://learnr.numbat.space.
• Make sure you have the latest R and RStudio installed.

💯 Course assessments

• 10% for Assignment 1 due Week 6
• 10% for Assignment 2 due Week 9
• 20% for Assignment 3 due Week 11
• 60% for final Exam scheduled later

All assessments are individual assessments. You may discuss questions (except for the exam) but you must provide the answer in your own words or your own code.