Introduction to Statistical Machine Learning COMP4670

Course overview

Course description

This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

Textbooks

Bishop, Christopher M. Pattern Recognition and Machine Learning , Springer

Attendance

Regular attendance expected; tutorials are crucial as they expand on the lectures.

Workload

Thirty one-hour lectures