This document provides an overview of the Machine Learning Advanced course. It discusses what machine learning is, common applications, and the different types of machine learning methods that will be covered, including supervised learning, unsupervised learning, and learning from sequential data. It also introduces some example problems that will be used, such as handwritten digit recognition, predicting heart disease, and analyzing DNA microarray data. Students will complete assessed exercises, a project, and an exam over the material presented.

1. COMP60431 Machine Learning Advanced Computer Science MSc Lecturers: Magnus Rattray & Gavin Brown

8. Methods: K-Nearest Neighbour or Support Vector Machines US Postal Service Digits

10. Example 2: Predicting heart disease (2% of full dataset shown) 1 3 0 2 1.6 0 125 0 0 149 112 4 0 71 1 3 1 1 0 0 159 2 0 303 128 4 0 57 2 3 2 2 2.6 0 145 0 0 234 134 1 1 61 1 3 0 1 0 0 180 2 0 235 140 3 1 44 1 7 0 1 0 1 111 2 0 226 142 4 1 53 1 7 0 2 0.5 0 161 0 0 234 135 4 1 59 2 7 3 2 4 0 154 2 0 407 150 4 0 63 …… 2 7 1 2 1.2 1 142 2 0 239 110 4 1 59 2 6 1 2 0.6 1 142 2 1 256 130 3 1 56 1 7 0 1 0.4 0 140 0 0 177 120 4 1 65 1 3 1 1 0.2 1 121 2 0 269 120 2 0 74 1 7 1 2 0.2 1 105 0 0 263 128 4 1 64 2 7 0 1 0.3 0 141 0 0 261 124 2 1 57 1 7 0 2 1.6 0 160 2 0 564 115 3 0 67 r typ maj slo old ang mx ele fb sc bp ch sex age

11. Example 2: Predicting heart disease “ Heuristics that make us smart”

14. Projection of 10,000 dimensional data onto 2D using PCA effectively separates cancer subtypes. DNA microarrays

20. Questions?

22. Method: Multilayer neural network