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Blekinge Institute of Technology
Department of Computer Science
Revision: 3
Reg.no:
Course syllabus
Machine Learning
Machine Learning
6 credits (6 högskolepoäng)
Course code: DV2599
Main field of study: Computer Science, Technology
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: 2023-01-30
Approved: 2023-01-30
1. Descision
This course is established by Dean 2019-11-12. The course syllabus is approved by Head of Department of Computer Science 2023-01-30 and applies from 2023-01-30.
2. Entry requirements
Admission to the course requires attended course in Applied Artificial Intelligence, 6 credits.
3. Objective and content
3.1 Objective
The main purpose of the course is to introduce theory and methods from machine learning and real-world applications from data mining. The technological development has increased our dependency on databases for storage and processing of information. The number and size of these databases grow rapidly. Due to this growth, it becomes more difficult to manually extract useful information. We therefore need semiautomatic and automatic methods to use, aggregate, analyze, and extract such information. Methods and techniques from machine learning, data mining, and artificial intelligence have been shown to be useful for these purposes.
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:
- independently and exhaustively define and describe solvable and tractable learning problems
- independently and broadly explain and summarize results from the application and evaluation oflearning systems
4.2. Competence and skills
On completion of the course, the student will be able to:
- independently modify or create and apply learning systems to different learning problems
- independently plan and execute experiments to evaluate and compare learning system
4.3. Judgement and approach
On completion of the course, the student will be able to:
- independently evaluate and compare learning systems for different learning problems given variousevaluation criteria
- independently evaluate and compare methods and measures for evaluation of learning systems
5. Learning activities
The education comprises lectures and laboratory sessions that together contribute to the theoreticalunderstanding and practical ability required to analyze, implement, and evaluate learning systems. The purpose ofthe laboratory sessions is to introduce platforms, tools and APIs for machine learning. The acquired knowledge isevaluated and increased through assignments, where subject-related problems must be solved either byimplementing custom learning systems or by applying existing tools. In addition, the course includes an individualproject in which a subject-related problem must be defined theoretically and solved practically according to thestate-of-practice and state-of-the-art. The solution, or solutions, must be evaluated/compared experimentally andthe results must be analyzed and summarized in a project report. The assignments and the project must beconducted individually. 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 |
| 2210 | Written assignment 1 | 1.0 credits | GU |
| 2220 | Written assignment 2 | 1.0 credits | GU |
| 2230 | Project Assignment | 4.0 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 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 Författare: Peter Flach
Förlag: Cambridge University Press Utgiven: 2012, Antal sidor: 396
ISBN13: 9781107096394 Reference literature
1. 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
2. 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
3. Probability and Statistics for Engineers and Scientists, Ninth edition / International edition
Author: Walpole, R., Myers, R., Myers, S., Ye, K.
Publisher: Pearson
Published: 2011, Antal sidor: 816
ISBN10: 0321748239
ISBN13: 9780321748232