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Introduction to Machine Learning

2016HT

Status Open for interest registrations
School Computer and Information Science (CIS)
Division STIMA
Owner Mattias Villani
Homepage https://www.ida.liu.se/~732A95/info/courseinfo.en.shtml

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Course plan

No of lectures

Approx 19 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

Fall 2015

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. Mixture models. Gaussian process.

Literature

- Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732.
- The Elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman, Second Edition, ISBN 9780387848587, pdf available for free.

Lecturers

Oleg Sysoev, Mattias Villani, Isak Hietala.

Examiner

Oleg Sysoev

Examination

Written report on individual computer labs.

Credit

6 hp

Comments


Page responsible: Director of Graduate Studies
Last updated: 2012-05-03