Description
Inductive modelling of data, especially counting models; ensemble approaches to modelling; maximum likelihood and density-based approaches to clustering, visualization. Applications to non-numeric datasets such as natural language, social networks, Internet search, recommender systems. Introduction to deep learning. Ethics of data analytics.
Follow-On Courses
This course appears in the pre- or co-requisites for the following course(s):
Learning Hours
120 (36 Lecture, 84 Private Study)
Prerequisite
Registration in a School of Computing Plan and a minimum grade of a C- (obtained in any term) or a 'Pass' (obtained in Winter 2020) in (CISC 271 and [3.0 units in STAT or STAT_Options]).