ML and AI drive the back-ends and front-ends of many large online companies, and are set to play a transformative role in the Internet of Things. This is a practical module that looks at how ML is applied to internet-scale systems. Topics covered will include A/B testing, ranking, recommender systems, and the modelling of users and entities that they engage with online (like news stories). Network effects, social networks, online advertising, and ML for real-time auctions will also be covered.
Upon completion of the module the student will be able to:
- design and build recommender systems, formulate key performance indicators (KPIs), and test for causality in systems that affect these KPIs;
- build mathematical and algorithmic models for internet users and entities that they engage with online;
- formulate and interpret the theory behind how ML is applied and deployed on internet-scale problems.