PGM is a tough book. I'm not sure it's the right book for "level 3" unless you want to be a level-3 who is good at PGMs. The problem with ML is there are so ma

PGM is a tough book. I'm not sure it's the right book for "level 3" unless you want to be a level-3 who is good at PGMs. The problem with ML is there are so many different kinds. Bishop's book is a decent light weight survey, but it doesn't come close to covering all the interesting fields. You could read that and Hastie/Tibshirani's book and still know almost nothing about online training (hugely important for "big data" and timeseries), reinforcement learning (mentioned, but not in any depth), agent learning, "compression" sequence predicting techniques, time series oriented techniques (recurrent ANNs for starters, but there is a ton to know here, and most interesting data is time ordered), image recognition tools, conformal prediction, speech recognition tools, ML in the presence of lots of noise, and unsupervised learning. I don't own PGM, but it probably wouldn't help much in these matters either. I know guys who are probably level 4 at machine learning who don't know about most of these subjects. On the other hand, Peter Flach's book "Machine Learning" at least mentions them and makes pointers to other resources. "Deep learning" is becoming kind of a buzzword for a big basket of tricks. I think it's worth knowing about drop-out training, and the tricks used to do semi-supervised learning, but the buzzword is silly. Technically "deep learning" just means "improved gradient descent." I figure level-4 is anyone making progress coming up with new techniques. That said, reading good books is one way to make progress. Knowing the right people is the other way.