| Details: This course provides advanced coverage of Machine Learning theory, concepts, techniques and their application to Knowledge Discovery and pattern recognition problems. Topics include: Supervised learning (parametric/non-parametric, support vector machines and neural networks), Unsupervised learning (dimensionality reduction, recommender systems and clustering) and best practices in machine learning (bias/variance and model selection). Prerequisites: Admission to the Masters of Science in Computer Science (F). |