I'm new to machine learning but am interested in decoding applications like the Higgs Boson challenge on Kaggle. I just wondered, what are the best algorithms or approaches for classifying such a small signal in large amounts of noise? I think supervised and unsupervised are equally fine to use for my project, but hearing about your thoughts in general would help a lot! I wanted to start with the Higgs Boson challenge to explore machine learning methods for improving discovery significance of scientific data. The experiment and data are available at https://www.kaggle.com/c/higgs-boson . The code used by the winner was released here: https://www.kaggle.com/c/higgs-boson/forums/t/10425/code-release . I know the basic theory of supervised and unsupervised learning, and have studied and used Linear Regression and SVM. I've read about neural networks also but on a basic approach level. I'm just learning about deep learning coming from the natural sciences, to improve data analysis, and found SVM very useful but otherwise don't know more advanced techniques. 
Classification in noise
machine learning