Machine Learning

Aim

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.

After completing the course the student should be able to:

• use relevant concepts and methods in machine learning to formulate, structure and solve practical problems.
• infer the parameters in a number of common machine learning models.
• use machine learning models for prediction and decision making.
• evaluate and choose among models.
• implement machine learning models and algorithms in a programming language.

Content

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. Cross-validation. Model selection. Overfitting. Bootstrap. Regression. Regularization. Ridge regression. Lasso. Variable Selection. Binary and multi-class regression. Dimension reduction. PCA. ICA. Kernel smoothers. Support Vector Machines. Decision trees. Gaussian processes. Mixture models.