• Session 1: Introduction to Machine Learning
• Session 2: End-to-end machine learning
• Session 3: Linear regression
• Session 4: Decision trees and ensemble methods
• Session 5: Automatic differentiation
• Session 6: Logistic regression
• Session 7: Neural networks
• Session 8: Unsupervised learning
• Session 9: Deep generative models
• Session 10: Advanced applications of machine learning

Details about the module delivery plan for COM4509 are here.

Follow this link for details on the lab sessions.

• Simon Rogers and Mark Girolami, A First Course in Machine Learning, Chapman and Hall/CRC Press, 2nd Edition, 2016.

• John D Kelleher, Brian MacNamee and Aoife D'arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, The MIT Press, 2015.

• Christopher Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, 2006.

• Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

• Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O′Reilly, 2nd Edition, 2019.

• Andriy Burkov, The Hundred-Page Machine Learning Book, 2019.

Some of the slides and lab notebooks used in this module are based on material developed by Prof. Neil Lawrence.