These sources introduce artificial intelligence, primarily focusing on machine learning as a method to achieve AI goals, using the relatable analogy of placing points and drawing lines to explain the core idea of pattern finding in data. They emphasize that understanding the problem and the available data types is crucial for choosing appropriate machine learning models, highlighting the necessity of good, clean data and the importance of data preprocessing steps like cleaning noisy data, handling missing values, and scaling features. The texts also touch upon different types of machine learning problems such as regression and classification, discuss concepts like the curse of dimensionality and techniques for dimensionality reduction, and briefly introduce neural networks and the concept of reinforcement learning while stressing the significance of domain knowledge and computational thinking for designers seeking to leverage these technologies. Finally, the need for GPU and parallel computing for efficient training is explained, along with an outline of a typical data-driven design process.

https://namjulee.github.io/njs-lab-public/work?id=2025-introductionToDesignComputation