| TBMI26 |
Neural Networks and Learning Systems, 6 ECTS credits.
/Neuronnät och lärande system/
For:
BME
CS
D
DAV
I
Ii
IT
KeBi
MED
MMAT
TB
Y
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Prel. scheduled
hours: 54
Rec. self-study hours: 106
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Area of Education: Technology
Main field of studies: Biomedical Engineering, Electrical Engineering, Computer Science, Computer Science and Engineering, Information Technology
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Advancement level
(G1, G2, A): A
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Aim:
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
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Requisite: Linear algebra, multivariable calculus, mathematical statistics.
Recommended: Signal theory, programming (Matlab).
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Organisation:
Lectures, lessons, assignments with mandatory written reports
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Course contents:
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.
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Course literature:
Stephen Marsland, Machine Learning: An Algorithmic Perspectiv
Compendium: examples, supplementary material, lab manual
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Examination: |
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Written examination
Laboratory work
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4 ECTS
2 ECTS
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Course language is Swedish/English.
Department offering the course: IMT.
Director of Studies: Marcus Larsson
Examiner: Magnus Borga
Link to the course homepage at the department
Course Syllabus in Swedish
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