For those considering an autodidactic alternative, this is for you. You can't go deeply into every machine learning topic. There's too much to learn, and the field is advancing rapidly. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal. If you're trying to force yourself forward, you'll slow down.
Machine Learning thrives on data. It is very important to understand the nature of underlying data on top of which the machine learning model is required to be built. Given a dataset, one of the first thing you would normally do is try to understand the nature and variety of data present in the dataset. This usually becomes the first stepping stone of creating a powerful and robust machine learning model. Statistics is a distant cousin of machine learning which also deals with data.
The human element of security includes people making mistakes, purposeful stealing of data or damage due to unauthorised use of accounts. In most systems there are lots of files logging activity such as login, web access, ip addresses, files and databases accessed. This data can be used to help'detect out of band' conditions that can trigger security alerts.