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Neural Networks and Learning Systems, 6 credits
Neuronnät och lärande system, 6 hp
TBMI26
Main field of study
Information Technology Computer Science and Engineering Computer Science Electrical Engineering Biomedical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Magnus BorgaDirector of studies or equivalent
Linda RattfältEducation components
Preliminary scheduled hours: 54 hRecommended self-study hours: 106 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer Science, Electrical Engineering, Biomedical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Computer Science and Engineering, M Sc in Engineering
- Industrial Engineering and Management - International, M Sc in Engineering
- Industrial Engineering and Management, M Sc in Engineering
- Chemical Biology
- Engineering Biology, M Sc in Engineering
- Applied Physics and Electrical Engineering, M Sc in Engineering
- Biomedical Engineering, Master's programme
- Computer Science, Master's programme
- Mathematics, Master's programme
- Information Technology, M Sc in Engineering
- Applied Physics and Electrical Engineering - International, M Sc in Engineering
- Computer Science and Software Engineering, M Sc in Engineering
Entry requirements
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.
Prerequisites
Requisite: Linear algebra, multivariable calculus, mathematical statistics.
Recommended: Signal theory, programming (Matlab).
Intended learning outcomes
The aim is that students after passing the course will be able to design and apply artificial neural networks and similar methods for signal, image and data analysis that learn from previous experience and data. Students will also be able to apply such methods to find meaningful relations in multidimensional signals where the degree of complexity makes traditional model-based methods unsuitable or impossible to use.
Specifically, students should be able to:
- Explain the difference between particular learning paradigms
- Implement and use common methods in those paradigms
- Select an appropriate method for solving a given problem
Course content
Machine learning, classification, pattern recognition and high-dimensional data analysis. Supervised learning: neural networks, linear discriminants, support vector machines, ensemble learning, boosting. Unsupervised learning: patterns in high-dimensional data, dimensionality reduction, clustering, principal component analysis, independent component analysis. Reinforcement learning: Markov models, Q-learning.
Teaching and working methods
Lectures, lessons, assignments with mandatory written reports
Examination
| LAB1 | Laboratory Work | 2 credits | U, G |
| TEN1 | Written Examination | 4 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för medicinsk teknikDirector of Studies or equivalent
Linda RattfältExaminer
Magnus BorgaCourse website and other links
http://www.imt.liu.se/edu/courses/TBMI26/Education components
Preliminary scheduled hours: 54 hRecommended self-study hours: 106 h
Course literature
Additional literature
Books
- Stephen Marsland, Machine Learning: An Algorithmic Perspective
Other
Compendium: examples, supplementary material, lab manual
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Laboratory Work | 2 credits | U, G |
| TEN1 | Written Examination | 4 credits | U, 3, 4, 5 |
Regulations (apply to LiU in its entirety)
The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.
LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva.
Additional literature
Books
Stephen Marsland, Machine Learning: An Algorithmic Perspective
Other
Compendium: examples, supplementary material, lab manual
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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TEN1
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| 1.2 Fundamental engineering knowledge (G1X level) |
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X
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X
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TEN1
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
X
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X
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TEN1
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
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X
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LAB1
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| 2.2 Experimentation, investigation, and knowledge discovery |
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X
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X
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LAB1
TEN1
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| 2.3 System thinking |
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X
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X
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TEN1
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| 2.4 Attitudes, thought, and learning |
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X
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LAB1
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| 2.5 Ethics, equity, and other responsibilities |
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
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X
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LAB1
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| 3.2 Communications |
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| 3.3 Communication in foreign languages |
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
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| 4.4 Designing |
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| 4.5 Implementing |
X
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X
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TEN1
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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