- 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.
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Simon Rogers and Mark Girolami, A First Course in Machine Learning, Chapman and Hall/CRC Press, 2nd Edition, 2016.
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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.
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Christopher Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, 2006.
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Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
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Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O′Reilly, 2nd Edition, 2019.
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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.