FIT5201 - 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, probabilistic, and generative models, non-parametric models (e.g., k-nearest neighbour, Gaussian process regression), k-means and latent variable models (e.g. Gaussian mixture model), expectation-maximisation, and neural networks and deep learning. Moreover, implementation techniques will be introduced and practiced that allow to practically implement the introduced algorithms in a scalable manner with robust and standardised interfaces.

Offerings

S1-01-CLAYTON-FLEXIBLE

S1-01-MALAYSIA-ON-CAMPUS

S2-01-CLAYTON-FLEXIBLE

S2-01-MALAYSIA-ON-CAMPUS

T3-57-OS-CHI-SEU-ON-CAMPUS

Requisites

Prohibition

Prerequisite

Rules

Enrolment Rule

Contacts

Chief Examiner(s)

Associate Professor Reza Haffari

Unit Coordinator(s)

Mr Loo Junn Yong

Learning outcomes

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

Describe the components and theoretical concepts of statistical machine learning;

Assess and explain theoretically the performance of machine learning approaches and derive recommendations for algorithm and model selection;

Derive and implement the most widely used machine learning models and algorithms and apply them to real-world and synthetic datasets;

Develop scalable and standardised implementations of typical machine learning algorithms using suitable programming techniques and libraries.

Describe and discuss ethical challenges when deploying machine learning systems in practice.

Teaching approach

Problem-based learning

Assessment summary

For on-campus offerings: 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

Seminars

Workload requirements

Workload

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/

Availability in areas of study

Advanced data analytics