Problem Solving using Pattern Recognition

Overview

Pattern recognition is one of the most important areas of AI. It is a branch of machine learning that focuses on the recognition of patterns and regularities in data. Pattern recognition systems can be trained from labelled training data through supervised learning and or unlabelled data through unsupervised learning.
This course is part of Graduate Certificate in Pattern Recognition Systems.

Course Description & Learning Outcomes

This course will be useful for participants to acquire pattern recognition knowledge. It will help participants analyse data more effectively by deriving useful hidden patterns in the data. Participants will also learn how to select and apply the most suitable pattern recognition techniques to solve the given problems and develop pattern recognition systems. At the end of the course, participants will be able to: - Model an applied problem as a pattern recognition task - Identify suitable pattern recognition techniques to solve the given problem - Assess and compare alternative pattern recognition methods for a given task - Analyse the results and suggest the possible improvement

Recommended Prerequisites

- Participants should be familiar with Python programming knowledge. - Foundational knowledge in statistics is strongly recommended. Participants with limited knowledge may consider acquiring them via Statistics Bootcamp II course

Pre-course instructions

- No printed copies of course materials are issued. - Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials. Registration close date: 21/08/2023