Machine Learning and Adaptive Intelligence

Neil D. Lawrence

For lab classes from 2013-14 please see here

Welcome to the COM4509/6509 Course on "Machine Learning and Adaptive Intelligence". This year the course has undergone a slight shift of focus relative to last year, in particular we will be introducing more emphasis on practical techniques for processing data using the Jupyter Notebook.

The lecture notes will all be given in the form of Jupyter Notebooks and are available below.

• Week 1 Probability and an Introduction to the Jupyter Notebook, python and Pandas.
• Week 2 Objective Functions: a simple example with matrix factorisation.
• Week 3 Linear Algebra and Linear Regression
• Week 4 Basis Functions
• Week 5 Review Week: Basis Functions & Linear Regression
• Week 6 Model checking: training, testing and validation
• Week 7 Probabilistic Interpretations of Objective Functions: Regression
• Week 8 Probabilistic Classification with Logistic Regression
• Week 9 Dimensionality Reduction: Latent Variable Modelling
• Week 10 Special Topics I
• Week 11 Review Week: Exam Practice
• Week 12 Special Topics II

Special topics are likely to be one or more of Gaussian processes, large scale learning and Support Vector Machines.