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Course Catalog 2010-2011
SGN-2806 Neural Computation, 5 cr |
Person responsible
Ari Visa
Requirements
Final exam, attendance at the classroom exercises and assignment.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
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Learning outcomes
Learning outcomes: The student can list the mentioned learning rules. The student can describe them and is capable to apply them to train neural networks. The student is capable to list to analyse the lectured neural networks (MLP,SVM,SOM and recurrent networks). The student is capable to analyse the own problem and to select the most suitable, lectured neural network. The student has a certain capability to create new solutions based on the lectured material.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Learning processes | ||
| 2. | Learning machines with a teacher | Multilayer Perceptrons Radial-Basis Function Networks Support Vector Machines Committee Machines | |
| 3. | Learning machines without a teacher | Principal Component Analysis with Neural Networks Self-Organizig Maps Boltzmann Machine | |
| 4. | Nonlinear dynamical systems | Temporal Processing Using Feed Forward Network Dynamically Driven Recurrent Network |
Evaluation criteria for the course
Exam and approx. 70% attendance at the exercises.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | "Neural Networks: a Comprehensive Foundation" | Haykin, S. | 2nd edition, Prentice-Hall Inc, 1999 | English |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
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Additional information
Lectures in English or in Finnish.
Suitable for postgraduate studies