The “Basic Training for Machine Learning" is an accompanying introductory training programme of the virtual “AI & Machine Learning Lab” for students participating in the ABC5-Lab, presented by the HKBCS and supported by the OGCIO.

Held over 7 weeks with interactive games and case studies, this programme teaches what machine learning (ML) is in the simplest terms, how ML is used now and in the future, and why ML knowledge is crucial for a good career in the 2020s. Each week's modules may be accessed asynchronously online, with live tutorials on the weekend and live ideation workshops twice a week on weekdays (limited in-person, rest via Zoom breakout rooms). All students of the entire school may participate.



Background

Driving either the 4th industrial revolution or the 2nd part of the digital revolution, an important type of AI called machine learning (ML) will reshuffle the socio-economic order across the world. While a plethora of programmes teaches how to use AI applications or operate AI-enabled equipment, few if any actually train students on the fundamentals of ML such that they can understand or design AI systems from nothing.



Scope

The scope of this programme gives students a solid foundation in ML by teaching:

  • What ML is in the simplest terms
  • How ML is used now and in the future
  • Why secondary students must be acquainted with ML i.e. why ML knowledge crucial for a good career in the 2020s

No prior skills or knowledge is needed to enroll in this ML course. This programme is designed for mass consumption, so ideally, all students of the entire school may participate.




List of Activities & Engagement



The programme is held over 6 or 7 weeks, unaffected by the pandemic:

  • Includes interactive games, case studies, short quizzes to gauge and engage continuously
  • Assessments may be used by teaching staff for their courses or future consumption
  • Duration: a total of 6 or 9 hours to complete, excluding tutorials and ideation
    • Self-paced: recommended completion date before April 4 (Easter 2021)
    • Modules may be accessed asynchronously online (between 1 to 2 hours each)
  • Each weekend has optional tutorials to let students ask about anything (up to 3 hours)
    • Students may switch between different topics, such as fintech, healthtech, regtech, etc
    • Limited attendance in-person, rest in teleconference breakout rooms


Students demonstrate their understanding by completing a capstone project

  • Students first pick a challenge/problem as a theme to accompany them on their learning journey
  • Students propose a data science application for a real-world challenge of their choice
  • Twice a week, ideation sessions are moderated by academic & industry mentors (up to 2 hours each)
    • Assisting students with their capstone projects at every stage of the process
    • Limited attendance in-person, rest in teleconference breakout rooms


Students may optionally build part of their proposed application or the whole with Lab tools:

  • Includes proprietary trust and privacy toolkits to deploy real-world applications
  • Includes example applications for demonstrating the concepts of machine learning
  • Includes machine learning architecture environments for prototyping applications (no coding)
  • Includes coding exercises and developer environments in Python and Julia


Springboard to advanced training:

Students who have finished our basic training course will be encouraged to participate in the “Competitions Preparation Training for ABCD Competitions” programme, which will primarily prepare students for:

  • Breakthrough Junior Challenge
  • (Hong Kong & International) Data Science Olympiad
  • The Ladies of Lovelace Awards
  • The Ross Programme
  • Trust Tech as the New Frontier


Main Objectives with Training for Machine Learning

  • Enable students to keep abreast of new technologies
  • Broaden students’ exposure to the IT industry and career
  • Enhance student’s ability to apply the IT knowledge and skills learnt
  • Foster student’s innovative abilities
  • Enrich students’ learning experience e.g. competitions, visits, project learning, etc.


This capstone project may manifest itself in the following forms:

  • Poster Board
  • Pitch Videos
  • Prototypes