ITI5201 - Machine learning - Monash University

Overview

This unit introduces machine learning and the major kinds of statistical learning models and algorithms used in data analysis. Learning and the different kinds of learning will be covered and their usage will be discussed. The unit presents foundational concepts in machine learning and statistical learning theory, e.g. bias-variance, model selection, and how model complexity interplays with model's performance on unobserved data. A series of different models and algorithms will be presented and interpreted based on the foundational concepts: linear models for regression and classification (e.g. linear basis function models, logistic regression, Bayesian classifiers, generalised linear models), discriminative and generative models, k-means and latent variable models (e.g. Gaussian mixture model), expectation-maximisation, neural networks and deep learning, and principles in scaling typical supervised and unsupervised learning algorithms to big data using distributed computing.

Offerings

MI-T2-6-INDONESIA-BLENDED

Requisites

Prerequisite

Prohibition

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Associate Professor Reza Haffari

Learning outcomes

On successful completion of this unit, you should be able to:

Describe what statistical machine learning and its theoretical concepts are;

Assess a typical machine learning model and algorithm;

Develop, and apply major models and algorithms for statistical learning;

Scale typical statistical learning algorithms to learn from big data.

Teaching approach

Problem-based learning

Assessment summary

This unit has threshold mark hurdles. You must achieve at least 45% of the available marks in the final scheduled assessment, at least 45% in total for in-semester assessments, and an overall unit mark of 50% or more to be able to pass the unit. If you do not achieve the threshold mark, you will receive a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Quizzes

2 - Assignment 1

3 - Assignment 2

4 - Scheduled final assessment (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Learning resources

Recommended resources

Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. http://www.springer.com/gp/book/9780387310732

Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009 (PDF freely available) http://statweb.stanford.edu/~tibs/ElemStatLearn/

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning: with Applications in R, Springer, 2013 ( PDF freely available ) http://www-bcf.usc.edu/~gareth/ISL/

David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012 (PDF freely available) http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

Ethem Alpadyn, Introduction to Machine Learning, MIT Press, 2004. https://mitpress.mit.edu/books/introduction-machine-learning

Kevin Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012. https://www.cs.ubc.ca/~murphyk/MLbook/