Machine LearningLinköping UniversityGoals The overall aim of the course is to provide an introduction to machine learning, with special focus on regression and classification problems. Machine learning is presented from a probabilistic perspective with inference and prediction based on probability models. The course aims to give students an overview of machine learning within a unified framework and a good basis for further studies in the field. Prerequisites Students entering the course should have passed at least one course in basic statistics and be familiar with linear statistical models, in particular simple and multiple regression. Also, it is a prerequisite that the students have passed courses in calculus and linear algebra. Organization Contents Introduction and overview of machine learning and its applications. Unsupervised and supervised learning. Discriminative and generative models. Prediction. Generalization. Classification. Nearest neighbors. Naïve Bayes. Discriminant analysis. Crossvalidation. Model selection. Overfitting. Bootstrap. Regression. Regularization. Ridge regression. Lasso. Variable Selection. Binary and multiclass regression. Dimension reduction. PCA. ICA. Kernel smoothers. Support Vector Machines. Decision trees. Neural networks. Deep learning. Literature  Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732. 
