Expressions of Interest - Machine Learning Academy 2025

We are launching an expression of interest for the Machine Learning Academy for 2025. This course is supported by the Accelerate Programme for Scientific Discovery and is run by Cambridge Spark. The Machine Learning Academy is a 12 month course which offers participants the opportunity to go further in using machine learning in their research.

The Academy offers a one-year programme of virtual learning, which will include online classes (and recordings); access to virtual learning material, such as coding assignments; mentorship; and discussion groups. Alongside teaching provision offered by Cambridge Spark, Accelerate Science supports the community with technical support from the AI Clinic and opportunities to engage with the AI for Science community.

Participants will need to commit 8-12 hours a month to the course and to complete 6 modules to pass the course. The course will run from January/February 2025 for 12 months and all participants must be employed by or registered as a student at the University of Cambridge for the duration of the course.

The course would normally cost participants £2,185.50 + VAT. For participants in 2025, Accelerate Science will be able to provide funding for 50% of the course cost, the remaining 50% costs will need to be met by participants. Please find further details about the course below and in the attached. If you are interested in joining the ML Academy, please respond complete the expression of interest form by 17:00, Friday 15 November. At this stage, we are seeking to assess levels of interest in the course, before running a full application process.

When submitting an expression of interest, you will be asked to confirm that you have approval from your PI to take part in the Academy and to commit the required time.

Further details:

What will the Academy cover? Academy participants will learn fundamental skills in machine learning that can help accelerate research. Topics covered by the Programme will include:

• Supervised and unsupervised learning
• Time series analysis
• Neural networks and deep learning
• Natural language processing
• Software engineering practices for data science
• Interpretability of models

How is this delivered?

The Academy will take place over the course of a year, during which participants will have access to learning materials from Cambridge Spark and enhancement activities convened by Accelerate. Virtual learning activities over the year will include:

• Live lectures/workshops, with recordings;
• Access to mentorship;
• Discussion groups and industry insights speaker events;
• Coding exercises and access to curated content delivered through the EDUKATE.AI platform.

Who is the course suitable for?

This learning pathway is designed for people already comfortable with Python and data processing in Pandas. The course covers advanced material and topics and so is suitable for those with a working knowledge of machine learning methods.

The course is open to PhD students and research staff who will be members of the University of Cambridge for the duration of the course (January 2025 – January 2026).

When will this take place?

The Academy starting in January/February 2025. After that date, participants will have a year of access to the online teaching materials. We will also plan learning support sessions and activities such as discussion groups, which will be driven in part by demand from participants. The course is structured with live virtual lectures each month totalling ~ 8-12 hours per month, there will be assignments for each module which are to be completed by the end of course.

What will this cost?

The course would normally cost £2,185.50 + VAT per participant. For this cohort of participants, joining in January/February 2025, Accelerate Science will be able to provide funding for 50% of the course cost meaning the cost to participants is £1,092.75 + VAT.

How can I find out more?

We are holding an information webinar with Cambridge Spark from 13:00 – 14:00 on Thursday 7 November. You can sign up to attend here.