Faculty of Information Technology
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science (http://online.monash.edu/course/graduate-diploma-data-science/?Access_Code=MON-GDDS-SEO2&utm_source=seo2&utm_medium=referral&utm_campaign=MON-GDDS-SEO2) via Monash Online.
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.
On successful completion of this unit, students should be able to:
On-campus: Examination (2 hours) 50%; In-semester assessment 50%
Monash Online: In-semester assessment: 100%
Minimum total expected workload equals 144 hours per semester comprising:
(a.) Contact hours for on-campus students:
(b.) Contact hours for Monash Online students:
(c.) Additional requirements (all students):
See also Unit timetable information
Advanced data analytics