Accelerate Programme for Scientific Discovery course timetable

Have you thought about using AI in your research but aren’t sure how to get started? Or are you already using AI and have run into challenges with implementation? Meet the Accelerate Programme's team of AI experts to find the support you need.

The Accelerate Programme's AI clinic is designed to help with challenging software issues a scientist encounters in all phases of the research pipeline when utilising machine learning. This includes issues related to: data collection, implementing privacy and compliance controls, data pipelines, model implementation, hardware/GPU matters, deploying models on the cloud, and packaging & publishing models.

We define a challenging software issue as one that is difficult to find online guidance/tutorials on, or basically one that you have attempted to resolve via multiple approaches but had no success in doing so.

No matter your level of experience with AI, we invite you to book a session and talk to our team to see how we can support you to implement AI in your research.

The clinic is open at any time for support so if you want to get in touch before your session or to book an earlier time, please email accelerate-mle@cst.cam.ac.uk.

Have you thought about using AI in your research but aren’t sure how to get started? Or are you already using AI and have run into challenges with implementation? Meet the Accelerate Programme's team of AI experts to find the support you need.

The Accelerate Programme's AI clinic is designed to help with challenging software issues a scientist encounters in all phases of the research pipeline when utilising machine learning. This includes issues related to: data collection, implementing privacy and compliance controls, data pipelines, model implementation, hardware/GPU matters, deploying models on the cloud, and packaging & publishing models.

We define a challenging software issue as one that is difficult to find online guidance/tutorials on, or basically one that you have attempted to resolve via multiple approaches but had no success in doing so.

No matter your level of experience with AI, we invite you to book a session and talk to our team to see how we can support you to implement AI in your research.

The clinic is open at any time for support so if you want to get in touch before your session or to book an earlier time, please email accelerate-mle@cst.cam.ac.uk.

This course will provide an introduction to Docker.

Writing research software in Python presents numerous challenges to reproducibility - what version of Python is being used? What about the versions of PyTorch, Scikit Learn or Numpy? Should we use Conda, or venv, or Poetry to manage dependencies and environments? How can we control randomness? Do I have the right version of Cuda Toolkit? In principle, given the same data, and same algorithms and methodology, we should be able to reproduce the results of any given experiment to within an acceptable degree of error. Dealing with the above questions introduces significant problems to reproducing experiments in machine learning. This workshop will explore the use of Docker to help alleviate almost all of these questions. Furthermore, combining Docker, git and GitHub can be a powerful workflow, helping to minimise your tech stack, and declutter your python development experience.

With the increase in AI-generated imagery using models such as Dall-E, Midjourney and Sora and research applications such as AlphaFold, there has been a surge in workflows incorporating models like Stable Diffusion. These models have potential in research applications including drug discovery, weather forecasting, synthetic speech and medical imaging.

The aim of the session will be to equip you with knowledge of how generative AI and diffusion models work and to share an overview of research applications. The workshop will include short talks from researchers already deploying diffusion models in their research.

Much of the workshop content is conceptual and high-level, and by the end of the day participants will have a firm grasp on how diffusion models work. We won’t be coding during the session, but will share code with you for you to work with after the session.

With the increase in AI-generated imagery using models such as Dall-E, Midjourney and Sora and research applications such as AlphaFold, there has been a surge in workflows incorporating models like Stable Diffusion. These models have potential in research applications including drug discovery, weather forecasting, synthetic speech and medical imaging.

The aim of the session will be to equip you with knowledge of how generative AI and diffusion models work and to share an overview of research applications. The workshop will include short talks from researchers already deploying diffusion models in their research.

Much of the workshop content is conceptual and high-level, and by the end of the day participants will have a firm grasp on how diffusion models work. We won’t be coding during the session, but will share code with you for you to work with after the session.

A one day workshop to equip researchers with knowledge of workflows and tools they can use to package and publish their code. Releasing software outputs from your research is an important step for open science and enables other researchers to utilise your code and for your work to have further impact. Participants will have the opportunity for hands on experience packaging and publishing a project.

Participants will require some background knowledge for this course. Experience using python for programming or scientific data analysis is required. You must also have a GitHub account. In addition, experience with building classes, using the command line (either Linux or MacOS), and some understanding of Git would be beneficial, but it is not required.

A one day workshop to equip researchers with knowledge of workflows and tools they can use to package and publish their code. Releasing software outputs from your research is an important step for open science and enables other researchers to utilise your code and for your work to have further impact. Participants will have the opportunity for hands on experience packaging and publishing a project.

Participants will require some background knowledge for this course. Experience using python for programming or scientific data analysis is required. You must also have a GitHub account. In addition, experience with building classes, using the command line (either Linux or MacOS), and some understanding of Git would be beneficial, but it is not required.

This is a 1-day workshop to equip you with knowledge of large language models (LLMs) for use in scientific research. The course will introduce LLMs and how they work. Next, we’ll discuss approaches to prompting and RAG, along with the methods that are used to finetune LLMs. The day concludes by covering responsible use and the landscape of models that are available to researchers with some of their pros and cons. After taking this workshop, you will be more confident to take the first steps in using LLMs in your own research.

Participants will require some background knowledge for this course. We will be looking at Python code and neural networks so familiarity with Python and some knowledge of machine learning and neural networks is required. We won’t be coding during the session, but will share code with you for you to work with after the session.

This is a 1-day workshop to equip you with knowledge of large language models (LLMs) for use in scientific research. The course will introduce LLMs and how they work. Next, we’ll discuss approaches to prompting and RAG, along with the methods that are used to finetune LLMs. The day concludes by covering responsible use and the landscape of models that are available to researchers with some of their pros and cons. After taking this workshop, you will be more confident to take the first steps in using LLMs in your own research.

Participants will require some background knowledge for this course. We will be looking at Python code and neural networks so familiarity with Python and some knowledge of machine learning and neural networks is required. We won’t be coding during the session, but will share code with you for you to work with after the session.

This is a 1-day workshop to equip you with knowledge of large language models (LLMs) for use in scientific research. The course will introduce LLMs and how they work. Next, we’ll discuss approaches to prompting and RAG, along with the methods that are used to finetune LLMs. The day concludes by covering responsible use and the landscape of models that are available to researchers with some of their pros and cons. After taking this workshop, you will be more confident to take the first steps in using LLMs in your own research.

Participants will require some background knowledge for this course. We will be looking at Python code and neural networks so familiarity with Python and some knowledge of machine learning and neural networks is required. We won’t be coding during the session, but will share code with you for you to work with after the session.

This is a 1-day workshop to equip you with knowledge of large language models (LLMs) for use in scientific research. The course will introduce LLMs and how they work. Next, we’ll discuss approaches to prompting and RAG, along with the methods that are used to finetune LLMs. The day concludes by covering responsible use and the landscape of models that are available to researchers with some of their pros and cons. After taking this workshop, you will be more confident to take the first steps in using LLMs in your own research.

Participants will require some background knowledge for this course. We will be looking at Python code and neural networks so familiarity with Python and some knowledge of machine learning and neural networks is required. We won’t be coding during the session, but will share code with you for you to work with after the session.

We know that when you’re learning AI & ML, a mix of classroom theory and hands-on practice is the best way to learn. So, we’re running a 1-day hands-on ML workshop to help you apply and develop further practical ML skills. This workshop will focus on Large Language Models (LLMs).

During this workshop, you’ll work in teams on a real dataset of your choice, with support from Accelerate Science Machine Learning Engineers and researchers. You’ll need to work on building, tuning and evaluating LLMs for your chosen dataset - we have some dataset ideas to kick you off, or you can bring your own. This is an opportunity to work on real-life ML problems and data, and gain confidence in using tools that you can take back to your own domain and research project.

This workshop is for people who are already confident with both ML fundamentals and Python programming. This isn’t a Python or ML introduction day - you’ll spend most of the day programming! We’ll use open-source libraries including HuggingFace and scikit-learn, so please come with a laptop and be prepared to get coding, before presenting your results to the group at the end of the day.

We know that when you’re learning AI & ML, a mix of classroom theory and hands-on practice is the best way to learn. So, we’re running a 1-day hands-on ML workshop to help you apply and develop further practical ML skills. This workshop will focus on Large Language Models (LLMs).

During this workshop, you’ll work in teams on a real dataset of your choice, with support from Accelerate Science Machine Learning Engineers and researchers. You’ll need to work on building, tuning and evaluating LLMs for your chosen dataset - we have some dataset ideas to kick you off, or you can bring your own. This is an opportunity to work on real-life ML problems and data, and gain confidence in using tools that you can take back to your own domain and research project.

This workshop is for people who are already confident with both ML fundamentals and Python programming. This isn’t a Python or ML introduction day - you’ll spend most of the day programming! We’ll use open-source libraries including HuggingFace and scikit-learn, so please come with a laptop and be prepared to get coding, before presenting your results to the group at the end of the day.