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Machine Learning, 7.5 Credits

Swedish name: Maskininlärning

This syllabus is valid: 2024-01-01 and until further notice

Course code: 5DV238

Credit points: 7.5

Education level: First cycle

Main Field of Study and progress level: Computing Science: First cycle, has at least 60 credits in first-cycle course/s as entry requirements

Grading scale: Three-grade scale

Responsible department: Department of Computing Science

Established by: Faculty Board of Science and Technology, 2023-09-13


This course is an introduction to machine learning and provides an overview of both theoretical and practical aspects of machine learning. The course introduces the basic concepts of machine learning and presents a range of different machine learning methods and models. The course also covers statistical and practical issues related to the design and evaluation of machine learning solutions.

The course consists of two parts:

Part 1: Principles (4.5 credits)

This part introduces the background and some important applications of machine learning. The following models and topics will be covered: Supervised learning (classification and regression with methods like support vector machines, naive Bayes, K-nearest neighbours, decision trees, neural networks), unsupervised learning (clustering and dimensionality reduction with methods like k-means clustering, hierarchical clustering, principal components analysis, linear discriminant analysis, density estimation), and learning theory (PAC-learning, bias/variance trade-off, regularisation). Furthermore, important concepts in machine learning, such as generative/discriminative learning, the maximum likelihood and Bayesian learning paradigms, and parametric/non-parametric learning will be discussed.

Part 2: Practice (3 credits)

This part consists of practical assignments that introduce modern machine learning libraries and development tools. The students will apply some of the machine learning methods/models covered in Part 1 to solve machine learning problems in realistic applications.

Expected learning outcomes

Knowledge and understanding
After completing the course, the student should be able to:

  • (FSR 1) explain and describe some concepts and methods central to machine learning such as classification, regression, clustering, dimensionality reduction, the bias/variance trade-off;

  • (FSR 2) categorize machine learning methods, e.g., supervised vs unsupervised, classification vs regression, clustering vs dimensionality reduction;

  • (FSR 3) explain the strengths and limitations of selected machine learning methods, and explain how and when they can be applied in different applications.

Competence and skills
After completing the course, the student should be able to:

  • (FSR 4) design and implement suitable machine learning solutions to given tasks in modern programming languages;

  • (FSR 5) apply state-of-the-art machine learning software libraries to solve applied machine learning tasks;

  • (FSR 6) evaluate the performance of machine learning methods using suitable metrics, e.g., accuracy, error rate, sensitivity, precision, recall

Judgement and approach
After completing the course, the student should be able to:

  • (FSR 7) discuss the impacts on the society of new technologies in machine learning.

Required Knowledge

At least 60 ECTS computing science or 120 ECTS within a program. (Students on a master's program leading to a degree in computing science are considered to fulfill this requirement.) At least 7.5 ECTS introductory programming; 7.5 ECTS data structures and algorithms; 7.5 ECTS mathematics including limits, derivatives, and probability theory; 7.5 ECTS linear algebra; 7.5 ECTS mathematical statistics.

Form of instruction

The instruction consists of lectures and practical assignments. In addition to scheduled activities, individual work with the material is required.

Examination modes

Part 1 (FSR 1, 2, 3, 7) is assessed by a written exam in halls and uses the grading scale Pass with Distinction (VG), Pass (G), or Fail (U).

Part 2 (FSR 4, 5, 6) is assessed by written assignments and uses the grading scale Pass (G) or Fail (U).

The course as a whole uses the grading scale Pass with Distinction (VG), Pass (G), or Fail (U). The grade on the course as a whole is determined by the grade on part 1.

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.


Valid from: 2024 week 1

Machine learning : a first course for engineers and scientists
Lindholm Andreas, Wahlström Niklas, Lindsten Fredrik, Schön Thomas
Cambridge : Cambridge University Press : 2022 : xii, 338 pages :
fritt tillgänglig pdf
ISBN: 9781108843607
Search the University Library catalogue
Reading instructions: The book can be bought in printed form or downloaded as a PDF (for free).