A First Encounter with Machine Learning (PDF) by Max Welling, Professor at University of California, Irvine.
From the preface:
In winter quarter 2007 I taught an undergraduate course in machine learning at UC Irvine. While I had been teaching machine learning at a graduate level it became soon clear that teaching the same material to an undergraduate class was a whole new challenge. Much of machine learning is build upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level. The situation was aggravated by the lack of a suitable textbook. Excellent textbooks do exist for this field, but I found all of them to be too technical for a first encounter with machine learning. This experience led me to believe there was a genuine need for a simple, intuitive introduction into the concepts of machine learning. A first read to wet the appetite so to speak, a prelude to the more technical and advanced textbooks. Hence, the book you see before you is meant for those starting out in the field who need a simple, intuitive explanation of some of the most useful algorithms that our field has to offer
This looks like a fun read!
Although I think an intuitive approach may be more important than as a prelude to more technical explanations.
In part because the machinery of technical explanations and their use, may obscure fundamental “meta-questions” that are important.
For example, in Jeremy Kun’s Homology series, which I strongly recommend, the technical side of homology isn’t going to prepare a student to ask questions like:
How did data collection impact the features of the data now subject to homology calculations?
How did the modeling of features impact the outcome of homology calculations?
What features are missing that could impact the findings from homology calculations?
Persistent homology is important but however well you learn the rules for its use, those rules won’t answer the meta-questions for its use.
An intuitive understanding of the technique and its limitations are as important as learning the latest computational details.
I first saw this at: Introductory Machine Learning Textbook by Ryan Swanstrom.