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ML@UCL
Projects and assignments undertaken during my master’s:
- COMP0086 Probabilistic and Unsupervised Learning (Gatsby Unit)
- Bayesian Model Selection
- Expectation-Maximisation
- Linear Gaussian State Space Models
- Markov Chain Monte Carlo decryption
- Gibbs Sampling for Latent Dirichlet Allocation
- COMP0085 Approximate Inference and Learning in Probabilistic Models (Gatsby Unit)
- Gaussian Processes
- Variational Bayes
- Expectation Propagation
- COMP0089 Reinforcement Learning (DeepMind)
- Dynamic Programming
- Monte Carlo Methods
- Temporal Difference Learning
- Function Approximation
- Policy Gradient Methods
- COMP0168 Machine Learning Seminar
- Gaussian Processes
- Bayesian Optimisation
- Modern Integration Methods
- Message Passing (Belief Propagation)
- Meta-Learning
- Paper presentation: GIBBON
- Paper review: Kernel Belief Propagation
- COMP0171 Bayesian Deep Learning
- Variational Autoencoders
- Bayesian Neural Networks
- Deep Generative Models
- Uncertainty Estimation
- COMP0078 Supervised Learning
- Kernel Ridge Regression
- k-Nearest Neighbours
- Perceptron, Support Vector Machines
- COMP0081 Applied Machine Learning
- Decision Trees, Random Forests, Gradient Boosting
- Kernelised SVM
- COMP0197 Applied Deep Learning
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Vision Transformers
- Group project: Image Segmentation using a Self-Supervised Vision Transformer Fine-Tuned on a Small Dataset