Plan of Activities
The Warden and Lead Instructor will accompany students throughout the programme, from airport arrival on Day 1 through departure on Day 7. All safeguarding standards are fully met, including enhanced DBS clearance and certified Mental Health First Aid training. A dedicated team of trained staff and student mentors will support participants during travel, social activities, and organised excursions, ensuring supervision and guidance at all times.
The programme combines rigorous academic sessions with applied workshops, designed to develop technical competence, analytical thinking, and collaborative problem solving. Each day concludes with structured group reflection, allowing students to consolidate learning, exchange perspectives, and build meaningful connections with peers and mentors.
1Day 1: Arrival in London
Sunday
A private group transfer will be arranged from Heathrow to the accommodation. Staff will be present at arrivals to assist students.
Estimated arrival at Princess Gardens (TBC): 16:00–16:30
Rooms:
Two/three students per room in separate beds. Male and female students accommodated separately.18:30–19:30 Dinner
19:30–20:30 Orientation & Programme Briefing
20:30–21:30 Informal Networking & Welcome Activities2Day 2: Fundamentals of Machine Learning
Monday
07:30-8:30: Breakfast
08:45 - Depart from accommodation to Lecture Rooms
09:00–10:40
Session 1: Introduction to Machine Learning
Supervised vs Unsupervised Learning
Regression and Classification
Linear Regression & Logistic Regression
Support Vector Machines (SVM) – intuition & geometry
Loss functions and optimisationIntegrated coding throughout using Python (NumPy, Pandas, Scikit-learn).
10:40–11:00 – Comfort Break
11:00–12:40
Session 2: Neural Networks in PracticeArtificial Neural Networks (ANN)
Gradient Descent & Backpropagation explained clearly
Model training and validation
Overfitting & Regularisation
Performance metrics and model evaluationLecture + coding combined in one continuous applied session.
1300-14:00 - Lunch Break
14:30-18:00 - Cultural Activity: Natural History Museum
19:00-20:00 - Dinner
3Day 3: Deep Learning Masterclass
Tuesday
07:30-8:30: Breakfast
08:45 - Depart from accommodation to Lecture Rooms
09:00-10:40
Session 1: Convolutional Neural Networks (CNNs)
From Artificial Neural Networks to Deep Architectures
Why Convolutions Work: Spatial Structure & Feature Hierarchies
Filters, Kernels, Stride, Padding explained visually
Feature maps and representation learning
Pooling layers and dimensionality reduction
CNNs for image classificationApplied Lab:
Implementing an image classifier in Python using TensorFlow or PyTorch.
Students train a simple CNN on a real dataset and evaluate performance.10:40–11:00 – Comfort Break
Session 2: Beyond CNNs – Sequence & Temporal Models
Limitations of feedforward networks
Introduction to Recurrent Neural Networks (RNNs)
Vanishing gradient problem
LSTM and GRU intuition
Applications in text, speech, and time series
Introduction to modern architectures and attention conceptsApplied Lab:
Predicting Google Stocks - implementing a case-study in Python14:30-18:00 - Cultural Activity: Natural History Museum
19:00-20:00 - Dinner
4Day 4: Agentic AI
Wednesday
07:30-8:30: Breakfast
08:45 - Depart from accommodation to Lecture Rooms
09:00–10:40
Session 1: Foundations of Reinforcement Learning
What makes an AI “agent”
Markov Decision Processes
States, actions, rewards
Exploration vs exploitation
Policy vs value based methods
Q Learning and Bellmen Equation10:40–11:00 – Comfort Break
11:00–12:40
Session 2: Agentic AI in Practice
Deep Reinforcement Learning
From Q Learning to Deep Q Networks
Autonomous decision making systemsApplied Mini Project:
Demo Teams
Students modify their agent to improve policy performance.
Performance evaluation and reward shaping14:30-18:00 - Cultural Activity: Natural History Museum
19:00-20:00 - Dinner
21:00-22:00 Board Games & Networking
5Day 5: Project Demo Day
Thursday
07:30-8:30: Breakfast
08:45 - Depart from accommodation to Lecture Rooms
09:00–10:40
Session 1: =Project Refinement
Teams finalise their AI models (ANN / CNN / RNN / Agentic systems)
Model evaluation and performance tuning
Debugging and optimisation
Preparing live demonstrations
Slide deck polishing and narrative structuring10:40–11:00 – Comfort Break
11:00–12:40
Session 2: Pitch Coaching & Rehearsals
How to explain technical systems clearly
Communicating model architecture and results
Demonstrating real world impact
Handling questions confidently
Final timed rehearsalsEach team completes a full dry run.
13:00–14:00 – Lunch Break
14:00–16:30
Trip to Imperial Innovation Hub (White City, up to 40 minutes drive)
19:00–22:00 – Gala Dinner (Formal Event in Central London)
6Day 6: Venture Strategy and Entrepreneurship
Friday
07:30-8:30: Breakfast
08:45 - Depart from accommodation to Lecture Rooms
09:00–10:40
Session 1: From AI Idea to Startup
Identifying real problems
Market validation
Product market fit
Business models in AI
Data as a strategic asset10:40–11:00 – Comfort Break
11:00–12:40
Session 2: Venture Strategy & Funding
MVP design
Monetisation strategies
Pitch deck fundamentals
Investment landscape
AI regulation and riskStudents refine a short venture concept built around their AI project.
13:00–14:00 – Lunch
14:30–17:30 – Cultural Activity
Shopping & Central London exploration19:00–20:00 – Dinner
20:00–21:00 – Closing Social & Reflection Session
7Day 7: Departure/
Handover
Saturday
07:30-8:30: Breakfast
9.30: Final checks and checkout
10:30: Photos
Departure to the airport closer to the flight time
Note:
If you intend to stay in the UK after the camp has ended, we will require a signed handover confirmation from your parent or guardian, as well as from the person responsible for you in the UK.
