Course Content
- Introduction to Pattern Recognition and Machine Learning Systems: Gain insights into the core principles and applications of pattern recognition and machine learning across diverse domains.
- Foundations of Neural Networks: Delve into the basic principles and workings of neural networks as the building blocks of machine learning systems.
- Neural Network Modeling and Design: Learn different neural network architectures tailored to specific problem domains.
- Deep Learning Systems: Explore advanced concepts and methodologies in deep learning, unlocking the potential for complex pattern recognition tasks.
- Convolutional Neural Networks (CNNs, ResNET): Dive into the architecture and applications of CNNs and ResNET for tasks such as image recognition and processing.
- Recurrent Neural Networks (RNNs, LSTMs): Understand the principles and applications of recurrent neural networks, including LSTMs, for sequential data analysis tasks.
- Hybrid and Ensemble Approaches: Discover techniques for combining multiple machine learning models, including hybrid and ensemble approaches, to enhance performance and robustness.
- Practical Case Studies and Workshops: Apply theoretical concepts through hands-on case studies and workshops, gaining practical experience in real-world scenarios.
This course is part of the
Artificial Intelligence and
Graduate Certificate in Pattern Recognition Systems Series offered by NUS-ISS.
Key Takeaways
- Evaluate and Contrast Pattern Recognition and Machine Learning Techniques: Analyse and compare a variety of advanced pattern recognition and machine learning methods to select the most effective solutions tailored to diverse problem domains.
- Harness Deep Learning for Complex Problem Solving: Utilise the power of deep learning to address intricate challenges, opening up new possibilities and enhancing your solutions.
- Design and Develop Intelligent Systems: Build intelligent systems from the ground up, leveraging deep learning and other sophisticated machine learning techniques.
- Optimise and Improve ML Models: Analyse the performance of your machine learning models, propose potential enhancements, and ensure continuous optimisation for maintaining peak performance.
Course Logistics
- No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
- Device Requirements: Bring an internet-enabled device (laptop, tablet, etc) with power chargers to access and download course materials.
If you are bringing a laptop, kindly refer to the table below for the recommended tech specs:
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Minimum
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Recommended
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Operating Systems
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• Windows 7 above
• Mac OS
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Laptop running the latest
version of either Windows or
Mac OS
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System Type
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32-bit
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64-bit
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Memory
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8 GB RAM
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16+ GB RAM
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Hard Drive
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256 GB disk size
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Others
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• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) |
DirectX 10 graphics card for graphics hardware acceleration
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Join Us
Enhance your expertise in machine learning. Register now to explore a variety of machine learning techniques and harness their potential.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
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WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.