Machine Learning and Adaptive Intelligence

Based on material from Neil D. Lawrence

For lab classes from 2015 please see here

Welcome to the COM4509/6509 Course on "Machine Learning and Adaptive Intelligence". Most of the material was developed by Prof. Neil Lawrence.

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 Reading Week
Week 6 Model checking: training, testing and validation
Week 7 Bayesian Regression
Week 8 Dimensionality Reduction: Latent Variable Modelling
Week 9 Probabilistic Classification
Week 10 Logistic Regression and Generalised Linear Models
Week 11 Reading Week
Week 12 Special Topic: Gaussian Processes

There are some refinements to the notebook slides required. Particularly for Week 8 (unsupervised learning) 9 and 10.

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