To be eligible, applicants should:

• Be between 16-17 years old.
• Have a sufficient understanding of English to participate in academic discussions and coursework.
• Have a keen interest in exploring their chosen subject(s).

If you meet these requirements, we’d love to see your application!

Applying is easy! Follow these steps:

1. Choose a Course: Select your subject, location, dates, and accommodation. You can add additional courses if you wish.
2. Provide Student Info: Fill in your name, contact details, and a short personal statement about why you'd be a great fit.
3. Enter School Info: Provide the name and address of your most recent school.
4. Review & Submit: Double-check your details and submit your application.

After submission, our admissions team will review your application, and you’ll hear back within 2-3 days. Apply now!

Yes, you can book more than one course! During the application process, you will have the option to add additional courses either before or after your main course. The total price, including any additional courses, will be displayed before you proceed.

After paying your deposit to secure your place, our admissions team will help you set up a bespoke payment plan. You can choose from options like monthly or bimonthly payments, and we will provide a detailed invoice outlining your instalment schedule. Learn more about payment plans.

Note: There are no additional fees for setting up a payment plan, and your place is secure once the deposit is paid.

The course is typically structured over two weeks, with each week focusing on different aspects of artificial intelligence and machine learning.

Week 1: Introduction to Python Programming and AI Fundamentals (Example)

• Introduction to Python programming.
• Foundations of Artificial Intelligence and Machine Learning.
• Data exploration and visualisation.
• Supervised learning algorithms.

Week 2: Machine Learning and Neural Networks with Python and PyTorch (Example)

• Unsupervised learning algorithms (e.g., clustering, principal component analysis).
• Neural networks and deep learning.
• Ethics and social impact of AI.

Note: This is an example structure. The actual content may vary based on the tutor’s expertise and student preferences. Students will have the opportunity to discuss their learning goals with admissions to tailor the course.

By the end of the course, students may:

• Understand the basics of AI and machine learning.
• Develop skills in Python programming for AI and data science applications.
• Explore supervised and unsupervised learning algorithms.
• Gain knowledge of neural networks and deep learning models.
• Analyse the ethical and social implications of AI technologies.

Note: Learning objectives may vary based on student interests, which will be discussed during the admissions process.

Students are encouraged to do pre-course reading to maximise their learning. Suggested resources include:

• Charles Severance, Python for Everybody: Exploring Data Using Python 3.
• Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
• Watch the introductory videos on AI and ML, such as this one.

Note: These are example resources. Specific materials may vary depending on tutor and student preferences. Students will be informed of the required materials in advance to allow enough time to prepare.

This course provides an excellent foundation for further studies in Artificial Intelligence, Data Science, and related fields. You will develop skills in Python programming, machine learning, and neural networks, which are essential for AI development. These skills are highly sought after in industries ranging from tech startups to research labs.

Note: Career exploration opportunities may vary based on the session and guest speaker availability.

You are not required to have prior knowledge of AI or machine learning, but you are expected to:

• Actively participate in seminars and tutorials.
• Complete independent research and assignments on AI topics.
• Engage with practical coding exercises and AI problem-solving challenges.

The course is designed to help you gain both academic knowledge and hands-on experience in AI and machine learning.

Note: The level of participation and engagement can be adapted to suit individual needs and preferences.

At Oxford Summer Courses, the safeguarding and wellbeing of our students aged 16-17 is our highest priority. All staff are fully trained in safeguarding policies and procedures, including enhanced DBS checks. We provide a safe and supportive environment with 24/7 on-site security, supervision when necessary, and curfews to ensure a secure atmosphere. Our policies are designed to offer age-appropriate care, balancing independence with safety.