Applied Machine Learning 2023

This class covers advanced topics in Machine Learning for Social Data Science. We extend the course provided in the first term's Introduction to Machine Learning class. This will extend the mathematical foundations towards domains where we are uncertain about the right answer or best approach.

This course emphasizes applications in a variety of domains and focuses foremost on strategies for high quality results from data. The topics we cover include : Creation of training and test datasets with techniques such as weak supervision, Transfer learning and fine-tuning models, the best practices for annotation, Bayesian approaches to estimation, Optimisation of hyperparameters using Bayesian optimisation and Techniques for interpretability and explainability.