Machine Learning, Neural Networks, and Melanoma

In high school, I was very interested in machine learning algorithms and investigated neural networks, support vector machines and unsupervised learning. I replicated various algorithms and tested their properties at different points in parameter space on the MNIST dataset which contains 60,000 handwritten numbers. Later, I used an unsupervised learning technique (Autoencoders, Hinton et. al 2006) to improve the performance of a support vector machine model in detecting Melanoma (skin cancer). My technique had a false negative rate similar to experienced doctors. I presented some of these results at ISEF 2007, 2008 and 2009.