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