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Teaching Fellow
Understanding Artificial Intelligence
Mathematics of Artificial Intelligence
Deep Learning
Applied AI
* theoretical foundations of learning representations
* efficiency of methods and models, and data efficiency
* transfer and small-data learning
* generative deep learning, unsupervised deep learning and stochastic optimisation
* scalable Bayesian methods
* biomedical and public health applications
* fairness and ethical application of ML
* Social computing AI and NLP
Data Science AI and Modelling Centre (DAIM)
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