Kickstarting Your Machine Learning Journey | SGInnovate
February 18-21
2019

Location

BASH, Level 3,
79 Ayer Rajah Crescent
Singapore 139955

Price

Early Bird (Ticket Inclusive of G.S.T) - $1642.4
Normal Ticket (Ticket Inclusive of G.S.T) - $1,712.00

Kickstarting Your Machine Learning Journey

Presented by SGInnovate & NUS-ISS

Together with the National University of Singapore – Institute of Systems Science, SGInnovate is proud 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 of it. 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 experimenting. This workshop will, therefore, focus less on mathematics and theory, and more on the practical aspects of getting started on experimentation. 

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

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

This workshop is eligible for funding support. For more details, please refer to the "Pricing" tab above.

Recommended Pre-requisites

  • Comfortable writing codes
  • Familiar with basic Python, NumPy and Pandas
  • Attendees MUST bring their own laptops

In this course, participants will learn 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

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

 

DAY 1

  • 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

  • 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

  • Coping with dimensionality
  • Individual project and checkpoint reviews

 

DAY 4

  • 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

 

Funding Support

This workshop is eligible for CITREP+ funding. 

CITREP+ is a programme under the TechSkills Accelerator (TeSA) – an initiative of SkillsFuture, driven by Infocomm Media Development Authority (IMDA).



*Please see ‘Guide for CITREP+ funding eligibility and self-application process’ section below for more information. 

Funding Amount: 

  • CITREP+ covers up to 90% of your nett payable course fee depending on eligibility for professionals

Please note: funding is capped at $3,000 per course application

  • CITREP+ covers up to 100% funding of your nett payable course fee for eligible students/full-time National Service (NSF)

Please note: funding is capped at $2,500 per course application

Funding Eligibility: 

  • Singaporean / PR
  • Meets course admission criteria
  • Sponsoring Organisation must be registered or incorporated in Singapore (only for individuals sponsored by organisations)

Please note: 

  • Employees of local government agencies and Institutes of Higher Learning (IHLs) will qualify for CITREP+ under the self-sponsored category
  • Sponsoring SMEs organisation who wish to apply for up to 90% funding support for course must meet SME status as defined here

Claim Conditions: 

  • Meet the minimum attendance (75%)
  • Complete and pass all assessments and / or projects

Guide for CITREP+ funding eligibility and self-application process:

For more information on CITREP+ eligibility criteria and application procedure, please click here

In partnership with:                                  Driven by:
        

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

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.

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

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