Course

Machine Learning (ELE520)

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


Dette er emnebeskrivelsen for studieåret 2020-2021. Merk at det kan komme endringer.

See course description and exam/assesment information for this semester (2024-2025)

Semesters

Fakta

Emnekode

ELE520

Vekting (stp)

10

Semester undervisningsstart

Spring

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Spring

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. Further to this neural networks are presented as a method to use when linear discriminant functions falls short. As part of this, deep neural networks will also be discussed, which is the foundation for deep learning. In the techniques presented this far, the class to which each data element belongs is assumed known. In the application of clustering techniques we no longer make this assumption and seek to find natural clusters in the data material. Finally methods for evaluating classifier performance are presented. Another important aspect of classification is how to characterize the data material as feature vectors. During the course illustrative examples from ongoing research projects within biomedical data analysis are presented.

Learning outcome

At the end of this course, the student should be able to recognize problems that can be handled by machine learning methods. Furthermore, the student shall be able to use the subject terminology acquired throughout the course to state the problem in a precise manner. To be able to solve the problem, the student must be able to implement a classifier by training it using a representative data material and make sure that it is capable of handling new data. The student should be able to handle different type of classifiers and know the theory for these so that specially designed solutions can be made.

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

Introduction to programming (BID230), Introduction to Programming (DAT110), Mathematical methods 1 (ÅMA100), Introduction to Probability and Statistics (ÅMA110), Mathematical Methods 2 (ÅMA260)
In the course the different methods are communicated by presenting and explaining the mathematical details. It is recommended that students who wish to follow the course should have solid prior mathematical knowledge especially in linear algebra and statistics. There is a lot of emphasis on the laboratory part of the course where one uses Scientific Python. Those who follow the course should therefore also have good programming skills, and must be prepared to write functions using iterative control structures and think about code reuse.

Exam

Form of assessment Weight Duration Marks Aid Exam system Withdrawal deadline Exam date
Home exam 1/1 Letter grades Inspera assessment


Vilkår for å gå opp til eksamen/vurdering

Exercises, Exercises
Mandatory assignments for getting access to the exam: 85% of lab assignments and 85 % of theoretical assignments must be approved by course instructor within given deadlines. 

Fagperson(er)

Course coordinator:

Trygve Christian Eftestøl

Head of Department:

Tom Ryen

Method of work

8 hrs per week with typically 4 hrs lectures and 2 hrs computer exercises and 2 hrs theoretical exercises per week.  There might be variations to this.

It is important to work with theoretical and practical exercises to be able to solve real world problems. 

Åpent for

Admission to Single Courses at the Faculty of Science and Technology
Data Science - Master of Science Degree Programme Computational Engineering - Master of Science Degree Programme Computer Science - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Industrial Automation and Signal Processing - Master's Degree Programme - 5 year Robot Technology and Signal Processing - Master's Degree Programme Cybernetics and Applied AI - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Emneevaluering

Form and/or discussion

Litteratur

Book

Machine learning : a Bayesian and optimization perspective Theodoridis, Sergios,, London ;; San Diego :, Elsevier ; Academic Press, 1 online resource (xxvii, 1031 pages) ;, [2020], isbn:0-12-818803-0, Curriculum ~~~~~~~~~~ Topic & Chapter -------------------------------------------------------------------- Introduction and background 1.1-1.6,2.1-2.3,A.1-A.2 Bayes decision theory 7.1-7.3 Parameter estimation 3.10 Non-parametric estimation 3.15,7.5 Linear discriminant functions 3.2,3.4,5.2,5.3,5.5 Neural networks 18 Clustering 12.6 Evaluation of classifiers 3.9,3.13 Feature extraction 3.12,19.3,7.7 The appendices are available from the book companion Website: https://www.elsevier.com/books-and-journals/book-companion/9780128188033 https://bibsys-ur.userservices.exlibrisgroup.com/view/uresolver/47BIBSYS_UBIS/openurl-UIS?ctx_enc=info:ofi/enc:UTF-8&ctx_id=10_1&ctx_tim=2021-01-05T14:21:53IST&ctx_ver=Z39.88-2004&url_ctx_fmt=info:ofi/fmt:kev:mtx:ctx&url_ver=Z39.88-2004&rfr_id=info:sid/primo.exlibrisgroup.com-BIBSYS_ILS&req_id=&rft_dat=ie=47BIBSYS_UBIS:5192749200002208,ie=47BIBSYS_HIOA:51105074050002212,ie=47BIBSYS_NETWORK:71589441460002201,language=eng,view=UBIS&svc_dat=viewit&u.ignore_date_coverage=true&user_ip=10.16.56.55&req.skin=primoView online

The course description is retrieved from FS (Felles studentsystem). Version 1