This summer, I had the incredible opportunity to attend two outstanding summer schools that significantly expanded my knowledge in machine learning and AI. Here’s my experience from both events.
ELLIS Probabilistic Machine Learning Summer School - Cambridge (July 14-18, 2025)
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The Cambridge ELLIS Unit Summer School on Probabilistic Machine Learning was an intensive deep dive into the foundations and cutting-edge developments in probabilistic machine learning. The program took place from July 14-18, 2025, at Pembroke College, Cambridge, featuring world-class lecturers and researchers.
Daily Program Structure: Link to heading
- Monday - Introduction: Foundational concepts in probabilistic machine learning
- Tuesday - Advanced Probabilistic Models: Deep dive into sophisticated probabilistic frameworks
- Wednesday - Generative Models: State-of-the-art generative modeling techniques
- Thursday - Causality: Causal inference and probabilistic approaches to causation
- Friday - Accelerate Science: Applications of probabilistic ML in scientific domains
The program is designed for graduate students, researchers, and professionals, featuring world-recognized experts speaking about advanced machine learning concepts.
The Cambridge setting provided an inspiring academic atmosphere, and the networking opportunities with fellow researchers from around the world were invaluable.
OxML Summer School - Oxford: Representation Learning & Generative AI (August 7-10, 2025)
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The Oxford Machine Learning Summer School (OxML) focused on MLx Representation Learning & Generative AI, taking place from August 7-10, 2025, in Oxford. This intensive program covered the latest developments in representation learning, including those behind the success of generative AI, with notable speakers from Google, University of Washington/Allen Institute for AI, Google DeepMind, Meta, and Hugging Face.
Key Topics Covered: Link to heading
- Natural Language Processing: Including Large Language Models and transformer architectures
- Computer Vision: Advanced visual representation learning and generative models
- Bayesian Machine Learning: Probabilistic approaches to deep learning
- Reinforcement Learning: Modern RL techniques and applications
- Geometrical Deep Learning: Graph neural networks and geometric approaches
- Generative AI: State-of-the-art generative modeling techniques
The hands-on workshops provided practical experience with state-of-the-art models, and the discussions with leading researchers in the field were particularly enlightening.