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Blekinge Institute of Technology
Department of Computer Science
Revision: 2
Reg.no: BTH-4.1.14-0212-2025
Course syllabus
Machine Learning
Machine Learning
6 credits (6 högskolepoäng)
Course code: DV2638
Main field of study: Computer Science, Software Engineering
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
Language of instruction: English
Applies from: 2025-03-13
Approved: 2025-03-13
1. Descision
This course is established by Dean 2023-05-03. The course syllabus is approved by Head of Department of Computer Science 2025-03-13 and applies from 2025-03-13.
2. Entry requirements
Admission to the course require completed credits in Programming, 5 credits, Data Structurse and Algorithms, 5 credits and Mathematical Statistics or Probability Theory, 5 credits. English 6.
3. Objective and content
3.1 Objective
The main purpose of the course is to introduce theory and methods from machine learning (ML) and real-world applications from data mining.
3.2 Content
The course comprises the following themes:
Introduction to Machine Learning: motivation, goals, theories, and existing methods as well as basic research and application trends.
Development of learning systems: planning, design, data preparation, implementation and testing of learning systems.
Directions and areas within ML: supervised learning, unsupervised learning, classification, meta learning, multi-instance learning, data mining applications.
Evaluation of learning systems: approaches, methods, and measures for evaluation and validation of learning systems.
4. Learning outcomes
The following learning outcomes are examined in the course:
4.1. Knowledge and understanding
On completion of the course, the student will be able to:
- define and describe solvable and tractable learning problems.
- broadly explain and summarize results from the application and evaluation of learning systems.
4.2. Competence and skills
On completion of the course, the student will be able to:
- modify or create and apply learning systems to different learning problems.
- plan and execute experiments to evaluate and compare learning systems.
4.3. Judgement and approach
On completion of the course, the student will be able to:
- evaluate and compare learning systems for various learning problems considering human, economical and societal benefits.
- valuate and compare methods and measures for evaluation of learning systems with respect to ethical aspects and reliability.
5. Learning activities
Education comprises lectures and laboratory sessions that together contribute to the theoretical understanding and practical ability required to analyze, implement, and evaluate learning systems. The purpose of the laboratory sessions is to introduce platforms, tools and APIs for machine learning. The acquired knowledge is evaluated and increased through assignments, where subject-related problems must be solved either by implementing custom learning systems or by applying existing tools. In addition, the course includes an individual project in which a subject-related problem must be defined theoretically and solved practically according to the state-of-practice and state-of-the-art. The solution, or solutions, must be evaluated/compared experimentally and the results must be analyzed and summarized in a project report. This course uses a learning platform for publication of course contents and information. The platform also hosts discussion forums, assignment and project submission, and feedback.
6. Assessment and grading
Modes of examinations of the course
| Code | Module | Credit | Grade |
| 2510 | Written assignment 1 | 1 credits | GU |
| 2520 | Written assignment 2 | 1 credits | GU |
| 2530 | Project | 4 credits | AF |
The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, Fx Failed result, a little more work required, F Fail.
The examiner may carry out oral follow-up of written examinations.
The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.
An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.
7. Course evaluation
The course evaluation should be carried out in line with BTH:s course evaluation template and process.
8. Restrictions regarding degree
The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.
9. Course literature and other materials of instruction
- Machine Learning: The Art and Science of Algorithms that Make Sense of Data
Author: Peter Flach
Publisher: Cambridge University Press
Published: 2012, Number of Pages: 396
ISBN: 978-1-107-09639-4
- Data Mining: The Textbook
Author: Charu C. Aggarwal
Publisher: Springer International Publishing Switzerland
Published: 2015, Number of Pages: 746
ISBN: 978-3-319-14141-1
- Machine Learning Engineering
Autor: Andriy Burkov
Publisher: True Positive Inc.; standardutgåva edition (5 Sept. 2020)
Published: 2020, Number of Pages: 310
ISBN-10: 1999579577
ISBN-13: 978-1999579579
- Neural Networks and Deep Learning: A Textbook
Author: Charu C. Aggarwal
Publisher: Springer International Publishing AG
Published: 2018, Number of Pages: 512
ISBN: 978-3-319-94462-3
- Introduction to Machine Learning with Python: A Guide for Data Scientists
Author: Andreas C. Müller and Sarah Guido
Publisher: O'Reilly Media; 1st edition
Published: 2016, Number of Pages: 398
ISBN-10: 1449369413
ISBN-13: 978-1449369415
10. Additional information
This course replaces DV2599