This is a L2 module for 2nd year undergraduate students at Durham University.
Linear Regression, Training and Loss, Generalisation, Optimisation, Training and Testing, Representation, Cost Functions, Binary Classifier, Performance Measurement, Odds and Logistic Regression, Maximum Likelihood, Naïve Bayes, Decision Tree, Ensemble Learning, Random Forests, Support Vector Machines, Kernel Methods, Dimensionality Reduction, Unsupervised Learning and Clustering, Gaussian Mixtures
This is a L3 module for 3rd year undergraduate students at Durham University.
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
This is a L3/L4 module, currently under construction, for students at Durham University.
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.