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Machine learning E-MDS110

This digital course focuses on methods for learning from data in order to train models that can make predictions when presented with new data. Such predictions can involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.

Publisert: Endret:
Facts
Credits (ECTS)

5

Next startup

Spring 2022

Level

Master

Application deadline

10.01.2022

Teaching method

Online

Cource fee

Free of charge except course litterature

Learning outcome

At the end of this course, you should be able to recognize problems that can be handled by machine learning methods. Furthermore, you will have learnt how to use the subject terminology acquired throughout the course to state the problem in a precise manner.

Machine learning

To be able to solve the problem, you will learn how to implement a classifier by training it by using representative data material and make sure that it is capable of handling new data. You will also learn to handle different types of classifiers and know the theory for these so that specially designed solutions can be made.

Course content

The course starts with an introduction to the fundamental theory, Bayes decision theory. This statistical based theory let us define optimal decision thresholds to distinguish between data elements, represented by so called feature vectors. These decision thresholds are optimal with respect to minimizing the expected error rate.

The introductory theory assumes that the statistical functions describing the data are known. This will not be true in practice where we will have to estimate these functions using parametric and non-parametric methods. Alternatively to estimating the statistical functions directly, we can estimate the coefficients in the polynomials describing the decision borders directly. This is introduced with linear discriminant functions where we seek to find the polynomial coefficients the rate of error expressed by a criteria function. To do this, we use iterative gradient descent techniques. Curve fitting by regression analysis is also presented in this context.

Moreover, neural networks are presented as a method to use when linear discriminant functions falls short, and finally, methods for evaluating classifier performance will be presented.

Mandatory requirements

Bachelor Degree 180 ECTS

Foreign applicants must also document education and English skills in accordance with NOKUT's regulations.

Recommended prerequisites

It is recommended that students who wish to follow the course should have some prior mathematical knowledge in linear algebra and statistics. The laboratory part of the course uses Scientific Python. Those who follow the course should therefore also have basic programming skills, preferably with Scientific Python.

Exam

Exam date: TBA

Coursework requirements

Exercises

Mandatory work requirements (such as theoretical exercises, laboratory assignments, project assignments and the like) must be approved by the subject lecturer within the specified deadline. The mandatory assignment plan must be approved in order to be admitted to the examination. Candidates who fail the compulsory practice program may not be able to complete this until the next time the course has ordinary teaching.

Lecturer

Professor
51832035
Det teknisk- naturvitenskapelige fakultet

Institutt for data- og elektroteknologi

Administrational contact persons

Senior rådgiver
51833046
Divisjon for utdanning

UiS etter- og videreutdanning
Førstekonsulent
51831501
Divisjon for utdanning

UiS etter- og videreutdanning