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Training

We’ve collected together some of the training courses and tutorials from the University and beyond that can help you get to grips with AI.

To help find the training that you need, we’ve grouped by level:

  • NEWCOMER You’re interested to learn about AI technology, but don't yet have a clear idea of how to use it. You are new to programming.
  • INTERMEDIATE You have some AI knowledge, have some clear ideas about how to use AI in your research, and consider yourself a a beginner- to mid-level programmer.
  • PROFICIENT You've successfully used AI in your research, are a competent programmer, and are looking to make research software and processes more scalable and efficient.

We welcome recommendations from our community, so do let us know if you come across other tutorials and courses that we ought to include here.

CONTACT US

Intro to AI

We have put together a short online course as an introduction to some of the core AI concepts.

  • AI Core Concepts
    NEWCOMER

    This short online course aims to introduce newcomers to some of the themes and concepts of AI. The goal is to help orient you in the field and signpost some next steps.

Courses: Accelerate Science & Cambridge Spark

These online courses are run jointly between the Accelerate programme and Cambridge Spark, and are available for university researchers to sign up to.

  • Python Programming for Science
    NEWCOMER

    1-3 day self-learning module introduces the fundamentals of Python, some of the kinds of data it can handle, and how to store that data. Designed for researchers across disciplines, it supports learners to rapidly learn how to code in the context of working with real-world data.

  • Data Residency
    NEWCOMER

    The Accelerate Programme offers PhDs and postdocs in disciplines across Cambridge University the opportunity to participate in a 5-week ‘Data for Science’ training course. This structured Accelerate-Cambridge Spark ‘Introducing Data Science for Research’ will equip scientists with modern practical data analysis skills using Python in a virtual instructor-led accelerated masterclass.

  • ML Academy
    NEWCOMER

    This 12 month introduction to Machine Learning is a structured Accelerate-Cambridge Spark course. With the fundamentals of AI and ML, and a selection of modules to choose from, the course is designed to give you the knowledge to confidently begin using ML in your work. Contact us to register your interest.

Accelerate Workshops

Accelerate's 1-day workshops are created by the Accelerate Programme's Machine Learning Engineers and researchers. You can sign up to attend the workshops when we run them, or work through the online material at your own pace.

University Courses

Other AI-related courses taught within the university

  • ML and Adaptive Intelligence
    NEWCOMER

    This course was originally delivered at the University of Sheffield (2011-2015), but has been updated with current material to introduce key concepts and methods in machine learning.

  • Advanced Data Science
    INTERMEDIATE

    This course looks at the real world challenges of data science, separating them into three stages: access, assess and address. The stages help in understanding that the data science pipeline is not just about the machine learning methods, but the ethical concerns, the challenges of data management as well as model fitting.

  • Data Visualisation
    INTERMEDIATE

    This lecture on data visualisation, together with code, can help you understand the different ways to look at data and to effectively showcase your research results.

  • Machine Learning and the Physical World
    PROFICIENT

    This course is focused on how to build machine learning systems that interact directly with the real world. It explores how to create models with a principled treatment of uncertainty, allowing researchers to leverage prior knowledge and provide decisions that can be interrogated.

  • Theory of Deep Learning
    PROFICIENT

    The objective of this course is to expose you to one of the most active contemporary research directions within machine learning: the theory of deep learning (DL). While the first wave of modern DL focussed on empirical breakthroughs and ever more complex techniques, attention is now shifting to building a solid mathematical understanding of why these techniques work so well in the first place.

Textbooks

We recommend the following textbooks to cover the basics of Machine Learning and AI:

Other resources from around the internet

  • Oxford University RSE training
    NEWCOMER

    The RSE team at Oxford University have created this online resource covering software engineering fundamentals that is useful for anyone learning programming for AI.

  • GitHub Hello World
    NEWCOMER

    Version control is a key part of the workflow for anyone writing software, to manage and track the code changes that you’re making. GitHub is a widely used version control tool, and their tutorial can help you get started.

  • Scikit-learn Documentation
    NEWCOMER

    Scikit-learn is one of the most popular open-source machine learning Python libraries, and their documentation is a helpful resource for understanding some ML concepts, with code examples to match.

  • PyTorch tutorials
    NEWCOMER

    PyTorch is an open-source ML library which is widely used by researchers. Their tutorials cover a number of examples, with code and notebooks available to build from.