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St Edmund's Global Programme - Programming for AI and Machine Learning

St Edmund's College, University of Cambridge

St Edmund's Global Programme - Programming for AI and Machine Learning

Official College Programme at the University of Cambridge

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Residential Course:

03-16 Aug 2025

4500

Online Course:

Disciplines:

Data Science

Science

Technology

Recommended English proficiency level:

Programming for AI and Machine Learning

Programme Highlights

Explore Careers & Global Perspectives

Students gain early insight into fields such as medicine, law, business, and technology, supported by hands-on workshops, industry speakers, and leadership development activities.

International Student Community

Learn alongside motivated peers from across the world, developing lifelong friendships and global awareness in a collaborative and inclusive environment.

Mentorship & Feedback

With small class sizes and caring teachers, students receive tailored support and feedback that helps build confidence and improve academic performance.

World-Class Faculty & Cambridge Expertise

Courses are delivered by academic experts and industry practitioners affiliated with the University of Cambridge, blending cutting-edge research with real-world insight.

⁠Career-Focused and Research-Enriched

Students explore future pathways through practical workshops, industry insights, and get exposure to Cambridge-style teaching.

Academic Programme

This intensive course covers essential programming and data science skills in AI and machine learning, starting with Python fundamentals and progressing to advanced topics in deep learning. Participants will work with popular data science libraries such as NumPy, Pandas, and Matplotlib, focusing on image processing and machine learning techniques like regression, clustering, and neural networks. Practical workshops and projects emphasise hands-on learning, allowing participants to develop expertise in both classical and deep learning methods, with bonus insights into computer vision, autoencoders, and language models.

Programme Goals

Experience working with Cambridge-based academic lecturers and researchers in the field of AI and machine learning.

Build foundational skills in Python programming and data science tools.

Understand key techniques in digital data processing and image analysis.

Develop hands-on knowledge in machine learning, from classical methods to deep learning.

Programme Components

Python Programming Fundamentals: Introduction to syntax, object-oriented programming, and local setup.

Data Science Libraries: Familiarisation with essential tools like NumPy, Pandas, and Matplotlib.

Digital Data Processing: Practical sessions in image data handling and feature extraction.

Machine Learning Fundamentals: Exploration of regression, clustering, and classification methods.

Deep Learning Techniques: Implementing neural networks for tasks such as digit and language model classification.

Your Life at College

Stay on campus in the historic St.Edmund’s  College. Founded in 1896, it is the second  oldest Cambridge college which accepts students aged 21 plus. St.Edmund’s is one  of the most international colleges of the University of Cambridge, with students coming from over 80 countries. You will be staying in a single en-suite bedroom with a shower and toilet.

More details on accommodation at St.Edmund’s College: 

st-edmunds.cam.ac.uk/college-life/accommodation-about-the-rooms/2533-2/

Empowering Students for Academic and Career Success

Earn a Valuable Cambridge Certificate

Global Credibility: A certificate from a University of Cambridge college signals world-class academic quality and enhances the professional reputation of your team.

Talent Attraction & Retention: Employees value development pathways that offer prestigious recognition, helping you recruit and retain top talent.

Client & Stakeholder Confidence: Certification demonstrates a tangible commitment to excellence and continuous improvement across your organisation.

Career-Ready Credentials: Participants gain a recognised academic endorsement that supports internal advancement and external credibility in competitive industries.

Enquire program

Program Schedule

Plan Your Success

Day #

Sunday

Arrival

Day #

Weekday

Section 1: Hello World

Introduction to Python and basic syntax (comment, basic arithmetic, import, datatype, etc.)

Introduction to the Jupyter environment on Google Colab/ Markdown language

Day #

Weekday

Section 2: Getting Local

Installation of the local Python environment (+ GitHub desktop)

Introduction to if/ for/ while/ python function

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Weekday

Section 3: Object Oriented

Introduction to the concept of object-oriented programming (OOP)

Concept of class

Day #

Weekday

Section 4: Libraries

Introducing libraries necessary for every data scientist

Key libraries: Numpy/skimage/ matplotlib/ pandas

Getting familiar with image data

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Weekday

Section 5: Fundamentals of Digital Data Processing

Getting familiar with image data

Part II- Introduction to Image processing

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Weekday

Section 6: Continue with Image Processing

Image processing

Part II - Image feature extraction

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Weekday

Section 7: Machine Learning Intro

Introduction to classical ML: Linear regression, dimension reduction, clustering, random forest

Object classification with ML tools

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Weekday

Section 8: Deep Learning Intro

Digits classification with classical ML method

Deep learning introduction- Implementing a DL algorithm - solving MNIST

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Weekday

Section 9: Deep learning- II

Implementing a DL algorithm - solving MNIST -II

Bonus: Autoencoders & Internal representation

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Weekday

Section 10: Deep learning- III

Bonus: Implementing a simple language model

Deep learning - the prospect

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Weekday

Final Day

Project Presentation

Traditional Afternoon Tea

Evening Reception

Formal College Dinner

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Departure

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Teachers

Expert Educators, Transformative Learning Experiences.

Prof. Pietro Liò

AI & Computational Biology Pioneer

Cambridge Professor using AI for precision medicine, Graph Neural Networks, and medical digital twins. Fellow at Clare Hall.

Learn More

Professor Pietro Liò is a Full Professor at the University of Cambridge, specialising in Computational Biology and a member of the Artificial Intelligence group. He holds an MA from Cambridge, a PhD in Complex Systems and Non-Linear Dynamics from the University of Firenze, and a PhD in Theoretical Genetics from the University of Pavia.

His research focuses on using AI and machine learning to develop models that integrate multi-scale biological and clinical data to address personalised and precision medicine. He is particularly interested in Graph Neural Networks, AI-based medical digital twins, and decision support systems.

He is a Fellow at Clare Hall College, a member of the Cambridge Centre for AI in Medicine, ELLIS, and the Academia Europaea. He also teaches Geometric Deep Learning and serves on several key committees, including the MPhil in Computational Biology programme.

Cheng-Yu (Kou) Huang

Computational Neuroscience

PhD candidate in the Department of Physiology, Development, and Neuroscience at the University of Cambridge

Learn More

Cheng-Yu (Kou) Huang is a PhD candidate in the Department of Physiology, Development, and Neuroscience at the University of Cambridge. His research interests lie in efficiently processing and interpreting multivariate biological data. More specifically, his current research in computational neuroscience focuses on understanding how spatial representations of the external world in the brain degrade in dementia and how these changes contribute to difficulties in spatial navigation. Beyond his primary research, Cheng-Yu is dedicated to scientific outreach and education. With a background in physics and biology, he frequently teaches data science and image analysis to biological researchers at institutes across Japan and Taiwan.

Matthew Ashman

Machine Learning

Matthew Ashman is a PhD candidate in the Machine Learning Group at the University of Cambridge. His research interests lie within probabilistic machine learning, and are are motivated by the need to improve decision making and prediction in the presence of uncertainty.

Learn More

Matthew Ashman is a PhD candidate in the Machine Learning Group at the University of Cambridge, where he is supported by the George and Lilian Schiff Foundation. His research interests lie within probabilistic machine learning, and are are motivated by the need to improve decision making and prediction in the presence of uncertainty. He has spent time working at Microsoft Research and the Australian National University. Prior to his PhD, Matthew received a MPhil in Machine Learning  and Machine Intelligence and a MEng  in Computer Engineering, both from the University of Cambridge.

Reviews

Hear From Those Who’ve Transformed Their Futures

The St Edmunds Global Programme was an eye-opening journey into business and entrepreneurship. Learning from Cambridge academics and collaborating with ambitious peers challenged me to think strategically, refine my problem-solving skills, and apply design thinking to real-world challenges. The entrepreneurship module, in particular, gave me the confidence to develop and pitch business ideas, shaping my approach to future opportunities. Beyond academics, the experience of living in Cambridge was truly inspiring. I left with new skills, valuable connections, and a clear direction for my future in business.

Daniel

St Edmund’s Global Programme 2023 Student

This programme provided the perfect balance of theory and hands-on application. As a post-graduate student, I needed a solid understanding of AI and machine learning without getting lost in technical jargon. The structured approach, combined with expert guidance from Cambridge academics, gave me the confidence to implement AI-driven strategies in my organisation. The practical workshops were especially valuable in bridging the gap between business and technology."

Joanna

St Edmund’s Global Programme 2024 Participant

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