Machine Learning (E-ELE501)

The course focuses on methods for learning from data for training models that can make predictions or classifications on new data.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

E-ELE501

Version

1

Credits (ECTS)

5

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

Norwegian

Offered by
Time table

View course schedule

Content

The course starts with an introduction to the fundamental theory, Bayes decision theory. This statisticaland mathematically based theory let us determine discriminant functions and through these optimaldecision functions to distinguish between data elements, represented by so called feature vectors.

Furthermore we will have to estimate the discriminant functions. This will be done by estimating theunderlying statistical functions directly or by estimating the functions's polynomial coefficients directly. Todo this, we use iterative gradient descent techniques. Curve fitting by regression analysis is alsopresented in this context. Further to this neural networks are presented and also some variants pf deep neural network architectures. Finally methods for evaluating classifier performance are presented.

Theoretical and laboratory exercises will accompany the progression through the topics.

Learning outcome

At the end of this course, the student should be able to recognize and handle machine learning problems.

The student should know the basic mathematical principles underlying the methods and to apply a software library like SciKit-Learn to solve problems. The student must be able to train a machine learning model using a representative data material and make sure that it is capable of handling new data.

Required prerequisite knowledge

None

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

Form of assessment Weight Duration Marks Aid
Home exam - Inspera 1/1 3 Hours Letter grades All

Individual exam

Coursework requirements

Compulsory assignments
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.

Course teacher(s)

Course coordinator:

Trygve Christian Eftestøl

Head of Department:

Tom Ryen

Overlapping courses

Course Reduction (SP)
Machine learning (E-MDS110_1) 5
Machine Learning (ELE520_1) 5
Machine learning, Machine Learning ( E-MDS110_1 ELE520_1 ) 10

Open for

Enkeltemner Teknisk-naturvitenskaplig fakultet - masternivå

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto