Course syllabus - Machine Learning Concepts
Scope
7.5 credits
Course code
DVA262
Valid from
Autumn semester 2022
Education level
First cycle
Progressive Specialisation
G1F (First cycle, has less than 60 credits in first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2022-01-24
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
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Introduction to machine learning, third edition [electronic resource] / Ethem Alpaydin
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ISBN: 9780262325745 LIBRIS-ID: 6j4934th4n014kzv
Mathematics for machine learning
Cambridge : Cambridge University Press, [2020] - xvii, 371 sidor
ISBN: 9781108470049 LIBRIS-ID: 0c1cjjz8x3849ghq
Objectives
The purpose of the course is to provide the participants with basic knowledge and conception of supervised and unsupervised machine learning and how they can be applied for classification and regression.
Learning outcomes
After completing the course, the student should be able to:
1. describe the fundamental needs, challenges, and limitations of machine learning,
2. describe and understand the basic principles of supervised learning for classification,
3. describe and understand the basic principles of supervised learning for regression and
4. describe and understand the basic principles of unsupervised learning.
Course content
- Fundamentals of Machine Learning.
- Mathematics for Machine Learning.
- Supervised Machine Learning for Classification: kNN, DT, Linear Models.
- Supervised Machine Learning for Regression: Linear Regression.
- Unsupervised Machine Learning: K-Means, Fuzzy c-Means.
Specific requirements
7,5 credits Object Oriented Programming on level G1F.
Examination
Laboratory work (LAB1), k-NN, 1 credit, examines the learning outcome 2, marks Fail (U) or Pass (G).
Laboratory work (LAB2), Decision Tree, 1 credit, examines the learning outcome 2, marks Fail (U) or Pass (G).
Laboratory work (LAB3), Logistic Regression, 1 credit, examines the learning outcome 2, marks Fail (U) or Pass (G).
Laboratory work (LAB4), Linear Regression, 1 credit, examines the learning outcome 3, marks Fail (U) or Pass (G).
Laboratory work (LAB5), Clustering, 1 credit, examines the learning outcome 4, marks Fail (U) or Pass (G).
Examination (TEN1), Written examination, 2,5 credits, examines the learning outcomes 1-4, marks Fail (U), 3, 4 or 5.
A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.
Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.
Grade
Pass with distinction, Pass with credit, Pass, Fail