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.

Jupyter Notebooks

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

References

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).

Acknowledgement

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