Teaching

Machine Learning

This is a L2 module for 2nd year undergraduate students at Durham University.

Topics to be covered:

Linear Regression, Training and Loss, Generalisation, Optimisation, Training and Testing, Representation, Cost Functions, Binary Classifier, Performance Measurement, Odds and Logistic Regression, Maximum Likelihood, Na├»ve Bayes, Decision Tree, Ensemble Learning, Random Forests, Support Vector Machines, Kernel Methods, Dimensionality Reduction, Unsupervised Learning and Clustering, Gaussian Mixtures

Recommender Systems

This is a L3 module for 3rd year undergraduate students at Durham University.

Topics to be covered:

History of recommender systems, information search and retrieval, filtering and personalising data content, users and transactions, item/user categorisation and characterisation, content-based filtering, collaborative filtering, data mining methods, context-aware methods, user similarity, matrix factorisation, alternating least squares, neighbourhood-based methods, recommender systems' challenges

Human-AI Interaction

This is a L3/L4 module, currently under construction, for students at Durham University.

Topics to be covered:

Perspectives on Human-AI Interaction, Designing AI/ML User Experience, Designing for Failure, Data and Knowledge, Data Visualization and Communication, Interpreting and Explaining Algorithms, AI Ethics, Fairness, Social Acceptability, and Trust, Human in the loop with AI/ML & Recommendations.