COM4509/6509 Machine Learning and Adaptive Intelligence - University of Sheffield
Autumn 2019 by Mauricio A Álvarez (1-5) and Haiping Lu (6-9)
- Session 1: Introduction to Machine Learning and Review of Probability
- Session 2: Objective functions
- Session 3: Linear regression
- Session 4: Basis functions
- Session 5: Generalisation
- Session 6: Bayesian regression
- Session 7: Unsupervised learning
- Session 8: Naive Bayes
- Session 9: Logistic regression
- Session 10: Other topics
Module delivery plan
Details about the module delivery plan can be found in this file.
To open a Jupyter Notebook, type on your terminal
jupyter notebook Lab 1 - Probability and Introduction to Jupyter Notebooks.ipynb
If you want to see the notebook as a slide show use the following instruction on your terminal
jupyter nbconvert Lecture 1-COM4509-6509.ipynb --to slides --post serve
Simon Rogers and Mark Girolami, A First Course in Machine Learning, Chapman and Hall/CRC Press, 2nd Edition, 2016.
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, 2017 (a new version to appear in Oct, 2019).
Most of the slides and lab notebooks used in this module are based on material developed by Prof. Neil Lawrence.