DV2578 Machine Learning

Programme course, 7,5 Higher education credits, Second cycle, autumn semester 2022

This course is part of a programme and cannot be applied.

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.

Facts

  • Type of instruction: On campus, day, part-time 50%
  • Period : 2022-October-31 until 2023-January-15
  • Education level: A1N
  • Application: This course is part of a programme and cannot be applied.
  • Language of instruction: The language of instruction is English.
  • Location: Karlskrona
  • Main field of study: Computer Science
  • Course syllabus: Download
  • Welcome letter: This course is part of a programme and has no welcome letter.
  • Entry requirements: Admission to the course requires attended course in Applied Artificial Intelligence, 7.5 ECTS.

Content

The course comprises the following themes:
Current and future learning systems:
motivation, goals, theories, and existing methods as well as basic research and application trends.
Development of learning systems:
planning, design, implementation, and testing of learning systems.
Directions and areas within learning systems:
supervised learning, unsupervised learning, classification, meta learning.
Evaluation of learning systems:
approaches, methods, and measures for evaluation and validation of learning systems.

Learning outcomes

Knowledge and understanding

  • independently and exhaustively define and describe solvable and tractable learning problems
  • independently and broadly explain and summarize results from the application and evaluation of learning systems

Competence and skills
  • independently and exhaustively modify or create and apply learning systems to different learning problems
  • independently and exhaustively plan and execute experiments to evaluate and compare learning systems

Judgement and approach
  • independently and exhaustively evaluate and compare learning systems for different learning problems given various evaluation criteria
  • independently and exhaustively evaluate and compare methods and measures for evaluation of learning systems

Course literature and other teaching material

Main literature
1. 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

Evaluating Learning Algorithms: A Classification Perspective
Authors: Japkowicz, N., Shah, M.
Publisher: Cambridge University Press
Published: 2011, Number of pages: 424
ISBN10: 0521196000
ISBN13: 9780521196000

2. Data Mining: Practical Machine Learning Tools and
Techniques, Third ed
Författare: Witten, I., Frank, E., Hall, Mark A. Förlag: Morgan Kaufmann
Utgiven: 2011, Antal sidor: 664
ISBN10: 0123748569
ISBN13: 9780123748560

3. Probability and Statistics for Engineers and Scientists, Ninth edition / International edition Författare: Walpole, R., Myers, R., Myers, S., Ye, K. Förlag: Pearson
Utgiven: 2011, Antal sidor: 816
ISBN10: 0321748239
ISBN13: 9780321748232

Course literature and other teaching material

Main literature
1. 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

Evaluating Learning Algorithms: A Classification Perspective
Authors: Japkowicz, N., Shah, M.
Publisher: Cambridge University Press
Published: 2011, Number of pages: 424
ISBN10: 0521196000
ISBN13: 9780521196000

2. Data Mining: Practical Machine Learning Tools and
Techniques, Third ed
Författare: Witten, I., Frank, E., Hall, Mark A. Förlag: Morgan Kaufmann
Utgiven: 2011, Antal sidor: 664
ISBN10: 0123748569
ISBN13: 9780123748560

3. Probability and Statistics for Engineers and Scientists, Ninth edition / International edition Författare: Walpole, R., Myers, R., Myers, S., Ye, K. Förlag: Pearson
Utgiven: 2011, Antal sidor: 816
ISBN10: 0321748239
ISBN13: 9780321748232

Learning methods

The course is campus-based. The 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.
The assignments and the project must be conducted individually. It is not allowed to collaborate with fellow students or others in any manner. If existing theory, methods, or tools are used, they must be clearly identified by motivation, citation, and description in the assignment submission or 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.

Work placement

No work placement is included in the planned learning activities. BTH is aiming for a close contact with the surrounding community when developing courses and programmes.

Assessments

Component examinations for the course
Code Title ECTS credits Grade
1810 Assignment 1 1 G-U
1820 Assignment 2 1 G-U
1830 Project 5.5 A-F

Grading

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Insufficient, supplementation required, F Fail.

Exams

More information about exams are found in the Student's Portal, where you also enrolls for most exams.


There might be other scheduled examinations. Information regarding these examinations are available in the learning platform Canvas or at other places that the person who is responsible of the course will refer to.

Course Evaluation

The course manager is responsible for the views of students on the course being systematically and regularly gathered and that the results of the evaluations in various forms affect the form and development of the course.

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