Course description

Welcome to Demystifying Machine Learning!

This is the very first edition of this I-Explore STEMM module, so congratulations on being our lab rats 🐀pioneers for the course!

This webpage will be the main portal for the course. Our learning materials can be found here.

Any downloadable materials (e.g. group project specifications) will be made available on Blackboard.

The Ed discussion board is also accessible via Blackboard. We will post all announcements on Ed. We also encourage you to use this forum for any questions or to generate discussions about machine learning among your colleagues.

There are two 'threads' to this course: theory and application.

  • The theory bit involves going through some prepared learning materials about the fundamentals of machine learning at your own pace.
  • The application aspect is a group project where you identify and solve a real problem (in a field of your choice) with machine learning.
The course is assessed via the group project (90%), along with a short self-reflection document towards the end of the course (10%).

More details about arrangements for the course will be given in the first live session on 19th January 4pm.

Course materials

For the 'theory' part of the course, you will go through our self-paced, guided learning materials:

classGuided Learning Materials

The link can also be accessed via the class button on the top right of the webpage.

Schedule

You will consume any of the prepared learning materials in your own time.

Thursdays 4-6pm are reserved for any live activities.

These live sessions will vary week by week (or may not even happen) - we may have consultation clinics, discussion sessions, project presentations or guest talks during these sessions. If there are no activities in a specific week, you may use the room for any group project discussions.

The schedule below is provisional and is subject to change.

Week Date Venue Activity
Week 1
(16/1 - 22/1)
Thu (19/1) 4-6pm SKEM 164 Live session: Introduction to module and group project and
Group project discussions
Week 2
(23/1 - 29/1)
Thu (26/1) 4-6pm CAGB 309 Consultation clinic
Week 3
(30/1 - 5/2)
Thu (2/2) 4-6pm CAGB 309 Assessment: Milestone 1 (Project pitch)
Fri (3/2) 7pm - Assessment: Deadline for peer assessments for project pitch
Week 4
(6/2 - 12/2)
Early in week - Mentors assigned to groups
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (9/2) 4-6pm CAGB 309 No live session - use room for your own discussions if needed
Week 5
(13/2 - 19/2)
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (16/2) 4-6pm CAGB 309 No live session - use room for your own discussions if needed
Week 6
(20/2 - 26/2)
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (23/2) 4-6pm HXLY 308 Guest talk: Benedetta Delfino, Machine Learning Tech Lead at Recycleye
Week 7
(27/2 - 5/3)
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (2/3) 4pm - Assessment: Milestone 2 (Promotional Website) deadline
Thu (2/3) 4-6pm HXLY 308 Guest talk: Joe Stacey, Imperial College London
Week 8
(6/3 - 12/3)
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (9/3) 4pm - Assessment: Deadline for peer assessments for promotional website
Thu (9/3) 4-6pm HXLY 308 Guest talk: Lee Clewley, Head of Applied AI Group at GlaxoSmithKline
Week 9
(13/3 - 19/3)
Arranged with mentor Arranged with mentor Weekly mentor meeting
Thu (16/3) 4-6pm HXLY 308 Assessment: Milestone 3: Technical Presentation
Week 10
(20/3 - 26/3)
Wed (22/3) 7pm - Assessment: Self-reflection Document deadline

 

Teaching Team

Josiah Wang

Josiah Wang

Course Leader

Edward Stevinson

Edward Stevinson

Project Mentor

Ryan Ong

Ryan Ong

Project Mentor

Tom Crossland

Tom Crossland

Project Mentor

Yichong Chen

Yichong Chen

Project Mentor