Machine Learning - ITEC873
This unit introduces basic machine learning techniques for constructing classifiers and regression models, focusing on widely applicable standard techniques such as Naive Bayes, decision trees, logistic regression and support vector machines (SVMs), and also including more general advanced frameworks such as graphical models. We discuss in detail the advantages and disadvantages of each method, both in terms of computational requirements, ease of use and performance, and study their practical application of these methods in a number of use cases.
| Credit Points: | 4 |
| When Offered: | S1 Day - Session 1, North Ryde, Day |
| Staff Contact(s): | Dr Rolf Schwitter, Dr Jia Wu |
| Prerequisites: |
Admission to MInfoTech or MSc or MDataSc or MCyberSec or GradDipInfoTech
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| Corequisites: | |
| NCCW(s): | |
| Unit Designation(s): | |
| Assessed As: | Graded |
| Offered By: | Department of Computing Faculty of Science and Engineering |
Course structures, including unit offerings, are subject to change.
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