What are the “hot algorithms” for machine learning?

This is a naive question from someone starting to learn machine learning. I’m reading these days the book “Machine Learning: An algorithmic perspective” from Marsland. I find it useful as an introductory book, but now I would like to go into advanced algorithms, those that are currently giving the best results. I’m mostly interested in bioinformatics: clustering of biological networks and finding patterns in biological sequences, particularly applied to single nucleotide polymorphism (SNP) analysis. Could you recommend me some reviews or books to read?


Deep Learning got a lot of focus since 2006. It’s basically an approach to train deep neural networks and is leading to really impressive results on very hard datasets (like document clustering or object recognition). Some people are talking about the second neural network renaissance (eg in this Google talk by Schmidhuber).

If you want to be impressed you should look at this Science paper Reducing the Dimensionality of Data with Neural Networks, Hinton & Salakhutdinov.

(There is so much work going on right now in that area, that there is only two upcoming books I know about that will treat it: Large scale machine learning, Langford et al and Machine Learning: a probabilistic perspective by Kevin Murphy.)

If you want to know more, check out what the main deep learning groups are doing: Stanford, Montreal and most importantly Toronto #1 and Toronto #2.

Source : Link , Question Author : J. Velazquez-Muriel , Answer Author : naught101

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