By Pedro Domingos, this focuses on the high-level algorithms, not on ethics (like http://doyoutrustthiscomputer.org). This pairs well with 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