Machine Learning Applications Engineering

This course covers all the basic concepts of Statistical Learning, focusing on the classical techniques of Machine Learning before the era of Deep Learning. These concepts include classification, (ridge) regression, (hierarchical) clustering, feature reduction and extraction, model selection and bootstrapping, fairness in  Machine Learning. The emphasis is on the concepts rather than mathematical details.

This course covers different verbal and nonverbal behavioural characteristics of conversation and embodied interaction, such as, intonation, gaze and gestures that humans show when communicating with both other people and machines. This behaviour is then related to different multimodal dialogue functions, including turn-taking, addressing others, and backchanneling, that give shape to the communication process. Topics on interaction with embodied agents and how to design memory models for conversational interaction are also discussed. You will apply this knowledge through the design, development, and evaluation of an embodied conversational agent application that uses a memory model to interact.

In this course you will learn how to systematically release your applications and make them scalable in an orchestrated environment and how to make your proof-of-concept applications production ready. This course takes you on a journey that starts with continuous integration and then moves on to continuous delivery, continuous deployment, and continuous experimentation. We will discuss the theory and the current research on various related subjects like containerization, testing, or monitoring and you will put the learned theory into practice. As a running example, you will build a pipeline for a machine learning application, which compared to traditional release engineering- poses additional challenges, like data versioning or model deployment.