If you’re confused about machine learning, or can’t tell the difference between a ‘principal component’ and a ‘cluster’: This talk is for you!
Modern software is often based around complex computational decision making. But how do programs make decisions? Is artificial intelligence just a complicated series of if-statements? And how is regression different from classification? Is deep learning better than other methods, and how can you decide if a machine learning model is good in the first place?
Required audience experience: No specific knowledge required.
Objective of the talk: This talks aims to bring some structure in the lingo of machine learning experts and targets absolute beginners.
Keywords: machine learning, basics, AI, decision making, deep learning, models, pca, principal component, cluster
You can view Boris’ slides here:
And his video here: