NUS-ISS Kickstarting Your Machine Learning Journey | SGInnovate
October 29-2
2018

Location

Perl @ BASH, Level 3
79 Ayer Rajah Crescent
Singapore 139955

NUS-ISS Kickstarting Your Machine Learning Journey

Presented by SGInnovate & NUS-ISS

SGInnovate partners with the National University of Singapore – Institute of Systems Science to bring to you an accelerated 4 days Machine Learning Workshop designed to kickstart your journey into this field. 

This workshop is designed for attendees with some technical skills e.g. software programmers, data analysts, developers who are comfortable with writing codes. Hit the ground running with hands-on machine learning experience through this workshop. 

Machine learning is a field of research that can take years to attain mastery. Through this workshop, you will experiment in the areas that are most relevant today.  The ultimate goal is to help you construct a learning map of areas to continue improving upon after each course. 

Machine learning practitioners spend a lot of time in experimentation. This workshop will, therefore, focus less on the mathematics and theory, and more on the practical aspects of getting started with experimentation.

By the end of the course, you will also be designing and implementing your own project to apply what you learned in your domain.

To get the most benefit from this course, you are expected to have the basic programming background in Python, and /or able to quickly self-learn Python along the way.

This workshop is pending funding approval. More details to be released soon. Please leave your contact details so that we can contact you the moment workshop is open for registration.

Course Takeaways

By the end of this course, attendees should be able to:

  • Describe the well-known machine learning techniques and applications
  • Apply well-known machine learning models using Python libraries for classification, regression, and clustering
  • Apply the machine learning workflow (data preparation, feature engineering, training, and validation) for supervised and unsupervised learning problems
  • Apply machine learning to a domain-specific problem of their choice, evaluate its effectiveness, and suggest further improvements

Recommended Pre-requisites

  • Comfortable in writing codes
  • Familiar with basic Python, NumPy, and Pandas

Agenda

Day 1
Topics Covered

  • Introduction to Machine Learning
  • Application of NumPy in representing and manipulating data
  • Application of Pandas in transforming and querying data
  • Application of Matplotlib in Data Visualization
  • Training your first machine learning model

Day 2
Topics Covered

  • Basics of training a machine learning model
  • Application of machine learning algorithms for data classification
  • application of machine learning algorithms for data clustering
  • Going green: trees, forests for supervised learning

Day 3
Topics Covered

  • Coping with dimensionality
  • Individual project and checkpoint reviews

Day 4
Topics Covered

  • Individual project and checkpoint reviews
  • Individual project presentations

This is an intensive and compressed version of this 25-day course: https://www.iss.nus.edu.sg/executive-education/course/detail/machine-learning-driven-data-science/stackup---startup-tech-talent-development

Instructors’ Biodata

Lisa Ong, Principal Lecturer & Consultant, Software Engineering & Design Practice NUS-ISS

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 includes writing and delivering operating systems code, building and deploying web services, and also building AI systems in recent years.

At Microsoft, Lisa has led and participated in many interesting projects including the Microsoft Embedded Learning Library, involved compressing and deploying computer vision and machine learning algorithms on tiny devices.  While part of the Windows product team, Lisa delivered operating system features such as a unified sensors API and driver stack, the Media Transfer Protocol stack, and geo-fencing capabilities on Windows OneCore. 

Before Microsoft, Lisa had a stint at Nuance Communications (USA) as an Embedded Software Engineer, working on small footprint text to speech systems.

For enquiries, please send an email to [email protected].

Topics: Artificial Intelligence / Deep Learning / Machine Learning / Robotics

You may also like the following: