Course syllabus - Machine Learning Concepts

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

Grading scale: 5, 4, 3