EdinburghAI Workshops
September 2026
A series of hands-on machine learning workshops designed and delivered for EdinburghAI, the University of Edinburgh's student-led AI society.
A series of hands-on workshops I designed for EdinburghAI, the University of Edinburgh’s student-led AI society. Two workshops have been delivered so far, with a third planned for September 2026.
Each session opens with a 15-minute lecture and slides covering the theory, followed by a hands-on Kaggle notebook where attendees work through the core deep learning logic themselves. A provided API handles the boilerplate so that the focus remains on the concepts that matter.
The series maps directly onto the technical stack behind Judo Clipper and was partly motivated by wanting to share what I learned while building it.
Workshop 1: Single Object Detection with YOLO
Attendees built a single-object YOLO detector in PyTorch from scratch, training it to locate raccoons in images.
The session covered:
- Converting images into tensors
- Normalising bounding-box coordinates
- Implementing confidence and coordinate loss
- Evaluating predictions using Intersection over Union
- Drawing predicted bounding boxes on held-out images
Attendees finished the workshop with a trained model capable of locating raccoons in unseen images.
Workshop 2: LSTMs, RNNs and Sequence Modelling
Building on the concepts introduced in Workshop 1, this session explored recurrent architectures for time-series data using an earlier version of the Judo Clipper pose dataset.
Attendees implemented both an RNN and an LSTM in PyTorch and trained them side by side. This allowed them to observe the RNN’s gradient explosion and compare it with the more stable convergence of the LSTM.
The session covered:
- Representing pose data as temporal sequences
- Implementing RNNs and LSTMs in PyTorch
- Understanding exploding gradients
- Using masked loss functions for missing pose detections
- Applying class-imbalance weighting
- Evaluating classification models using F1 score
Workshop 3: Content Recommendation Algorithms
Planned for September 2026.
This upcoming session will explore the algorithmic foundations of short-form content recommendation.
Planned topics include:
- Collaborative filtering
- Content-based recommendation
- User and content representations
- Ranking candidate content
- Balancing relevance, novelty and diversity
- How short-form platforms surface content at scale
Resources
All workshop materials are available through the following repositories: