Cambridge Machine Learning Society

Machine learning is transforming our world. We aim to connect, inspire, and support students and staff across Cambridge with the hopes of improving everyone's understanding of this field.

What We Do

We aim to connect, inspire, and support students and staff across Cambridge with the hopes of improving everyone's understanding of the field through talks, workshops, and discussions so that we can all make well-informed decisions about our future and leverage this technology for the greater good of humanity.

You don't have to be from a STEM background to get involved, the more people that can bring their unique backgrounds and perspectives to this field, the better.

Why We Do It

Machine learning is transforming our world in more ways than we can imagine. This incredible technology can be of great service to humanity yet also pose a major threat to our existence. We believe that, by promoting both an active interest and understanding of machine learning to people of all backgrounds, we can use of this technology for good.

Learn More

If you want to learn more, come to our events. We look forward to meeting you!

The Committee


Ray, a second-year student, is a software engineer and machine learning enthusiast. When not fine-tuning his models' hyperparameters, he can be found making videos, devouring productivity books, and getting involved in access work.


Paul, a second-year student, is always ready to explore machine learning with others. When not studying, you will most likely find him game developing, playing squash or dabbling with Python.


Miguel, a second-year student, is a former e-sports champion with a keen interest in machine learning. When away from his desk, he can be found playing his guitar or electric keyboard to the newest K-pop releases.

Contact Us

Have an idea to make our society better? Want us to feature some content? Or simply want to say hi? Do not be afraid to drop us a message at or use the contact form below.


We are delighted that are you interested in learning more about machine learning. To help you in your journey, we have compiled a number of resources that we found useful. Remember that you don't have to use them all; we recommend you pick one you like and stick to it.

Deep Learning for Coders with fastai and PyTorch is by far the best book we've come across for learning through interactive Jupyter Notebooks.
For these courses, an understanding of Python will be useful. We recommend using freecodecamp to learn the basics then practising your skills by doing challenges on HackerRank or working on your own projects. teaches how a top-down hands-on approach of machine learning (which many believe to be the best approach) through a book and series of great lectures by Jeremy Howard.Machine Learning by Stanford is often the go-to course for getting an understanding of the fundamentals of most machine learning techniques. It goes well with the courseMachine Learning Crash Course by Google is a great short course to help you understand the basic concepts with some great interactive visualisations.CS50's Introduction to Artificial Intelligence forms part of the famous CS50 that Harvard runs.
To stay update to date with the field, we often listen to:
Data Skeptic in which Kyle Polich interviews and discusses interesting topics with practitioners in the field.The TWIML AI Podcast in which Sam Charrington interviews many great guests every week.Chai Time Data Science in which Sanyam Bhutani interviews many great practictioners every week.
Alignment Newsletter written weekly by a Rohin Shah, a PhD Student at UC Berkly, offers many highlights, opinions, summaries, and predictions in the field.Import AI by Jack Clark focuses on recent developments in the AI and includes a 'why this matters' subsection to encourage readers to think about implications.
YouTube Channels
Two Minute Papers by Karoly Zsolnai-Feher explains many recent papers in an engaging and lively manner which helps us all get excited about the field and limitless possibilities.
If you know of resources that we may like then please let us know at