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Introduction to Machine Learning

2014HT

Status Active - open for registrations
School Computer and Information Science (CIS)
Division Statistics
Owner Oleg Sysoev
Homepage Not available yet

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Course plan

No of lectures

Approx 10 lectures, 9 computer labs and 5 seminars.

Recommended for

Anyone with an interest in learning the basics of machine learning.

The course was last given

Never before.

Goals

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

Basic concepts in machine learning and data mining. Bayesian and frequentist modelling, model selection. Linear regression and regularization. Linear discriminant analysis and logistic regression. Bagging and boosting. Splines, generalized additive models, trees, and random forests. Kernel smoothers and support vector machines. Gaussian process.

Literature

Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

Lecturers

Oleg Sysoev, Patrik Waldmann, Josef Wilzén.

Examiner

Oleg Sysoev and Patrik Waldmann.

Examination

Written report on individual computer labs.

Credit

6 hp

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Last updated: 2012-05-03