The theme of this talk is centered around Machine Learning from an engineering perspective. Here, accuracy is often the least interesting aspect of model selection, and very few real-world use cases depend on some 0.01% improved accuracy on ImageNet. This talk will demonstrate a particular project at Ericsson for predicting mobility in telecommunications networks. The use case will be used as a demonstrator in terms of the impact of model selection on the final task, and the sometimes very large implications a seemingly simple model choice will make on the final solution. If time (and audience) permits, the speaker will also go through a custom wait-free implementation of a sparse graph utilizing approximate updates implemented for this particular use case.
Speaker
Jesper Derehag, Sr. Machine Learning Engineer @ Ericsson AB
Schedule
17.00 - Doors open, and mingle
18.00 - Talk: Machine Learning in practice
19.30 - Food & mingle
Important info
You have to register at the Ericsson reception to be let in. The reception closes at 18, so you have to pass the gate before that time.
