Issues with Machine Learning – Approximating the Truth - Part 2
Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. This lesson is part two of the module on machine learning. It covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth.
Part one of this two-part series can be found here, and covers the history and ethics of AI, data, statistics and variables, notation, and supervised learning.
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Michael began programming as a young child, and after freelancing as a teenager, he joined and ran a web start-up during university. Around studying physics and after graduating, he worked as an IT contractor: first in telecoms in 2011 on a cloud digital transformation project; then variously as an interim CTO, Technical Project Manager, Technical Architect and Developer for agile start-ups and multinationals.
His academic work on Machine Learning and Quantum Computation furthered an interest he now pursues as QA's Principal Technologist for Machine Learning. Joining QA in 2015, he authors and teaches programmes on computer science, mathematics and artificial intelligence; and co-owns the data science curriculum at QA.