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Who should attend
- IT professionals who need to apply pattern recognition techniques for developing intelligent systems for varied applications, including machine vision, business analytics, etc.
- IT professionals who wish to obtain knowledge in pattern recognition to add more value and insights to their systems/solutions.
- Domain specialists and others who plan to undertake pattern recognition projects.
About the course
Pattern recognition is one of the most important areas of Artificial Intelligence. 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.
Pattern recognition has been widely used to solve many real-world problems such as image processing, speech recognition, data mining, business analytics, etc. There are many pattern recognition techniques available to perform different tasks such as regression, classification, clustering, etc. using various statistical and machine learning algorithms.
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.
This course is part of the Artificial Intelligence and Graduate Certificate in Pattern Recognition Systems Series offered by NUS-ISS
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
What Will Be Covered
- Introduction to problem solving using pattern recognition
- Solving classification and prediction problems
- Solving clustering and anomaly detection problems
- Component analysis and dimension reduction
- Deep learning basics
- Practical case studies and workshops
Charles lectures and consults on artificial intelligence, knowledge engineering and knowledge management. He has been a principal investigator, project manager, supervisor and developer for many knowledge based systems project in Canada and Singapore. Previously, he was a research scientist with ...
Dr. Zhu Fangming is with the Institute of Systems Science of the National University of Singapore (NUS-ISS). He currently lectures in the Master of Technology programme in the areas of evolutionary computation, neural networks and data mining. Prior to joining ISS, he was a postdoctoral fellow i...
Jen Hong develops algorithms. He specializes in deep learning, image processing and medical image diagnosis. He designs illustrations, web page and posters. He plays piano. He invented a mathematical model to analyze dry eye. He used deep learning to correct medical images. He trained deep learni...
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