By Pedro Domingos, this focuses on the high-level algorithms, not on ethics (like http://doyoutrustthiscomputer.org). There is more recent work from this author, and other points of view (like Rod Brook's post on AI Progress). The main idea off the book is breaking down different strategies of machine learning:
- Chapter 2 - The argument for evolution - saying ‘'evolution is an algorithm'' seems a little simplistic - from Sapiens it seems like a large number of random events have lead to the here and now.
- Chapter 5 - From Naive Bayes to Hidden Markov chains to Kalman filters in 3 pages!
- Chapter 7 - most recommendation systems are some sort of nearest-neighbor
- Curse of dimensionality - Dimensions higher than 3 explained with pealing a hyperspace orange
- what is a Support Vector Machine
- Chapter 8
- Putting together birds of a feather - several pages to describe k-means
- Discovering the shape of the data - Principal Component Analysis - lining data up, reducing dimensions
- Learning to Relate - What are Relational Learning algorithms?
- Chapter 9
- Chapter 10
- Epilogue - Machine Learning examples