This repository contains a collection of coursework from my MSc in Machine Learning at UCL (2021/2022). Content of the various folders ranges from reports, paper reviews, coding exercises and end-to-end projects.
Below is a summary of the various topics tackled in the various projects present here.
Kernelisation of algorithms, sample complexity analysis, study of generalisation behaviour of common algorithms (perceptron, winnow, knn,...). Kernel perceptron from-scratch implementation and analysis on MNIST.
Group project on argument mining (argument component identification to be precise). The full codebase for the project can be found here.
Review of Deep Kernel Learning. Group presentation on Neural Importance Sampling
Miscellaneous coursework covering the entire module, from bandit algorithms to Deep Reinforcement learning using JAX and haiku.
Summative coursework on Bayesian Belief Networks, inference on graphs, LDPC codes and more.
Bayesian classifiers and posterior approximation. Variational Autoencoders. Uncertainty quantification and calibration in pre-trained Deep Neural Networks.
From-scratch implementation of CART trees, random forests, ADAboost, Support Vector Machines.