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It is certainly a "dummie's guide" approach and probably the best book applying this framework to machine learning related problems. It introduces a lot of ide

by brent 18y ago

It is certainly a "dummie's guide" approach and probably the best book applying this framework to machine learning related problems. It introduces a lot of ideas to people where the math would simply not be approachable. That said I have three complaints: 1 I think it tries to stay too high level on some areas where it is truly unavoidable and wouldn't scare anyone off to add a little depth. 2 I think it too often labels their implementation with a name that is associated with a family of techniques. This is simply going to embarrass/mislead the reader. In the interest of maintaining simplicity the book states several things that are flat out wrong. 3 No references. If someone wants to take the next step there is zero guidance. This is important because someone learning about a deep topic could read the book and a) think they understand it (not likely) and b) not have a clue where to look next.

j2d2 18y ago
Can you elaborate on the things it simply gets wrong?
- brent 18y ago
  One example, simply because I opened the book to this page after reading your comment and happen to know a bit about the subject (p49): "[t]he algorithm takes the difference between every pair of items and tries to make a chart in which the distances between the items match those differences" This is referring to multidimensional scaling which is a large family of techniques, some of which attempt to achieve this goal (delta ~= d), but not necessarily. Granted, in this case the algorithm presented DOES attempt to do this. BUT, the algorithm presented is not "multidimensional scaling". It is an implementation (not even a good one) attempting to minimize one of many possible objective functions. I noticed several more when I flipped through it initially, but this is the type of thing I'm referring to.
me2i81 18y ago
I thought the lack of references was a major failing of the book. Some of the algorithms barely scratch the surface, and it does a disservice to the reader to provide noplace to go. Here's a few to get started: 1. Russel and Norvig's AI text, 2. Elements of Statistical Learning by Hastie et. al., 3. Pattern Recognition and Machine Learning by Chris Bishop. On the other hand going right into code examples is useful, including jumping right into getting real data downloaded and worked on.
- brent 18y ago
  I agree about #2. I have Russell and Norvig and agree that it is an excellent book, but I am not sure how much overlap there truly is here. I also do not have PRML, but Neural Networks for Pattern Recognition by Bishop is excellent (and includes many non-NN related items). ESL is excellent and, to me, the best modern text in machine learning. It covers many of the topics in PCI both at a reasonable level and in much more depth and provides MANY references (100s?) to dig deeper.

by brent 18y ago

j2d2 18y ago

Can you elaborate on the things it simply gets wrong?

brent 18y ago
One example, simply because I opened the book to this page after reading your comment and happen to know a bit about the subject (p49): "[t]he algorithm takes the difference between every pair of items and tries to make a chart in which the distances between the items match those differences" This is referring to multidimensional scaling which is a large family of techniques, some of which attempt to achieve this goal (delta ~= d), but not necessarily. Granted, in this case the algorithm presented DOES attempt to do this. BUT, the algorithm presented is not "multidimensional scaling". It is an implementation (not even a good one) attempting to minimize one of many possible objective functions. I noticed several more when I flipped through it initially, but this is the type of thing I'm referring to.

me2i81 18y ago

I thought the lack of references was a major failing of the book. Some of the algorithms barely scratch the surface, and it does a disservice to the reader to provide noplace to go. Here's a few to get started: 1. Russel and Norvig's AI text, 2. Elements of Statistical Learning by Hastie et. al., 3. Pattern Recognition and Machine Learning by Chris Bishop. On the other hand going right into code examples is useful, including jumping right into getting real data downloaded and worked on.

brent 18y ago
I agree about #2. I have Russell and Norvig and agree that it is an excellent book, but I am not sure how much overlap there truly is here. I also do not have PRML, but Neural Networks for Pattern Recognition by Bishop is excellent (and includes many non-NN related items). ESL is excellent and, to me, the best modern text in machine learning. It covers many of the topics in PCI both at a reasonable level and in much more depth and provides MANY references (100s?) to dig deeper.