Machine Learning, 6 credits

Main field of study

Course level

Advancement level

Course offered for

• Computer Science, Master's Programme
• Computer Science and Engineering, M Sc in Engineering
• Information Technology, M Sc in Engineering
• Computer Science and Software Engineering, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Probability theory; Statistics; Mathematical analysis; Linear Algebra; Basic programming.

Intended learning outcomes

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.

Course 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.

Teaching and working methods

The course consists of lectures and computer laboratory work. The lectures introduce concepts and theories that students then use in problem solving at the computer labs.

Examination

Grades

Department

Director of Studies or equivalent

Examiner

Education components

Course literature

Additional literature

Books

• Bishop, C. M, (2006) Pattern Recognition and Machine Learning Springer
• Hastie, T., Tibshirani, R., och Friedman J., (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction 2:a upplagan Springer