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+traditional approaches
+- hand crafted and curated
+- based on intuition not evidence (e.g., password criteria example)
+- static and difficult to scale and adapt
+
+Everything we do generates data. This can be used to learn what works and what doesn't
+
+We use the data to evolve from observations to models
+
+data doesn't lie. humans do (sometimes using data)
+
+ai uses machine learning, data science applies machine learning. statistical learning is machine learning
+
+# ML
+using mathematics and algorithms to build models from data for prediction and insight
+
+key concepts
+- getting dta
+- feature enginering
+- model testing/validation
+- knowledge extraction
+
+supervised vs unsupervised learning — often feed into each other
+
+![classic machines learning map|400](https://i.imgur.com/zQj8D2e.png)
+
+## feature engineering
+converting domain variables into actionable features
+
+e.g.,
+- edges from images
+- word frequencies in documents
+- converting TCP stream into counts of particular packet types
+
+
+## insights
+modelling uncovers relatonships within inderlying process represented by the data
+
+e.g., detecting a password attack
+
+benefits
+- provide insight
+- automattion
+- adapt to changes
+- scale larger and more complex problems
+
+# threats
+![today's threats example|300](https://i.imgur.com/4q6tbVS.png)
+
+need to secure all of them - smartly
+
+