UBC ML Kaggle Competition



Learning from an advanced machine learning course, I was able to implement my own algorithms at low levels. We analyzed decision trees, naive bayes, knn, ensemble methods, random forests, kmeans, DBSCAN, hierarchical, regression, gradient descent, RBF, perceptron, SVM, stochastic gradient, various boosting methods, MLE, MAP, PCA, matrix factorization, dimension reduction, compression, convolutional neural networks. I finished 2nd in the final class Kaggle competition, with my neural network. Archived Github upon request.