NICF- Feature Extraction and Supervised Modeling With Deep Learning (SF)

NUS Institute of Systems Science

NUS Institute of Systems Science

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Who should attend

Any professional from domains (e.g. healthcare, finance, manufacturing) that need to manage and work with data.

Data engineers, researchers, healthcare professionals and more.

About the course

Machine Learning uses techniques to deal with data in the most intelligent way – by developing algorithms – to derive actionable insights. With machine learning, you can glean useful patterns from the deep, focused troves of data specific to your chosen domain. Analysing that data provide insights that can drive successive new waves of efficiency and automation, reducing operational costs and potentially pinpointing new sources of revenue.

  • Concepts and intuition: Participants learn and apply the concepts of machine learning using a methodology. You will learn to navigate smoothly through the data sciences and machine learning space by not only creating but also debugging your products with ease. You will then apply the same concepts covered in a real world scenario while in class.
  • Architecture: Participants learn what is required to architect a data science platform / team and how to effectively design a machine learning driven data sciences product using a wide range of techniques that are taught and practised in class. This includes, but is not limited to, understanding different parts of a data science product. The main goal of this course is to provoke thinking, establish the context of learning with the objective of developing and enhancing your capabilities in establishing a machine learning product.
  • Implementation: Finally, participants will have the opportunity to apply the concepts using industry standard libraries and tools to develop their own machine learning driven data science project.

Key Takeaways

Upon completion of the course, the participants will be able to:

  • Compare the difference between Deep Learning and Machine Learning
  • Explain the applications of deep learning
  • Use industry standard deep learning frameworks to create deep learning models
  • Adapt existing deep learning models to new applications

What Will Be Covered

Topics

  • Introduction and applications of Deep Learning
  • Deep Learning python libraries
  • Neural Networks
  • Convolutional neural networks
  • Transfer learning
  • Applications such as medical image processing, smart / IoT applications

Course Agenda

Day 1

  • Bridging Recap: Supervised and Unsupervised Machine Learning Modeling
  • Lecture: Introduction to Deep Learning
  • Workshop: Setting up Deep Learning Tools and Environments

Day 2

  • Lecture: Neural Networks and Back Propagation
  • Workshop: Neural Networks and Back Propagation

Day 3

  • Lecture: Convolutional Neural Networks
  • Workshop: Convolutional Neural Networks

Day 4

  • Lecture: Transfer Learning and Fine Tuning
  • Workshop: Transfer Learning and Fine Tuning

Day 5:

  • Guest Lecture: Medical Image Processing using Transfer Learning
  • Workshop: Project Work

Day 6

  • Guest Lecture: Deep Learning on Smart Applications
  • Workshop: Project Presentations

Experts

Lisa Ong

Lisa is with the Software Engineering and Design Practice, StackUp program for National University of Singapore, Institute of Systems Science (NUS-ISS). Lisa has multiple years of extensive experience in software product research and development at Microsoft Corporation (USA). Her background in...

NICF- Feature Extraction and Supervised Modeling With Deep Learning (SF) at NUS Institute of Systems Science

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.

We are happy to help you find a suitable online alternative.