The proliferation of malware and the increased availability of computation devices has made malware detection an important task. Large quantities of malware belonging to various families are produced every day. Modern methods like polymorphism and metamorphism are being used to obfuscate malware which makes them difficult to detect using static analysis. Dynamic analysis techniques are also being used through executing in a sandbox environment and analyzing the behaviour of the malware. This method is difficult to apply to a large number of files owing to the time intensive process. This project aimed at exploring machine learning for the classification of Portable Executable PE files into either malware or benign categories. This projects explores the design and implementation of spatial pyramid pooling algorithm in convolutional neural networks as a means of allowing variable sized malware PE file images. This project also works on classification of malware classes using portable executable files. A comprehensive dataset composed of both malware and benign files is also developed as part of this project.