Summary
In this chapter, we set the foundations of AI for the rest of this book. We covered some important topics:
- What AI is and its shift toward AGI.
- How ML creates models adaptively by ingesting data and how it is the brain of AI. This makes it the focus of adversarial AI attacks and defenses.
- The different types of ML based on how models learn – that is, supervised, unsupervised, and reinforcement learning.
- The seven typical steps in the ML life cycle, which include data collection and pre-processing, selecting an algorithm based on the problem we are solving, model training, testing and evaluation, fine-tuning and optimization, and, finally, deploying and using the model.
- Key ML algorithms and where they are used. This included linear and logistic regression, decision trees, and their ensemble version with random forests in supervised learning. We looked at K-means clustering and PCA, two popular unsupervised models, and Q-learning in reinforcement...
