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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
FFR135 - Artificial neural networks
 
Syllabus adopted 2014-02-26 by Head of Programme (or corresponding)
Owner: MPCAS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Chemical Engineering, Engineering Physics
Department: 16 - PHYSICS


Teaching language: English
Open for exchange students
Block schedule: B

Course elements   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0100 Written and oral assignments 7,5c Grading: TH   7,5c    

In programs

MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 2 (elective)
TKITE SOFTWARE ENGINEERING, Year 3 (compulsory elective)

Examiner:

Professor  Bernhard Mehlig

Course evaluation:

http://document.chalmers.se/doc/6facd30c-f224-4ae5-91f7-0b90d89c1f7d


Homepage missing

 

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

Sufficient knowledge of Mathematics (analysis in one real variable, linear algebra), basic programming skills.

Aim

Neural networks are distributed computational models inspired by the structure of the human brain, consisting of many simple processing elements which are connected in a network. Neural networks are increasingly used in the engineering sciences for tasks such as pattern recognition, prediction and control. The theory of neural networks is a inter-disciplinary field (neurobiology, computer science and statistical physics).
The course gives an overview and a basic understanding of neural-network algorithms. can develop an understanding of when neural networks are useful in application problems

Learning outcomes (after completion of the course the student should be able to)

understand and explain strengths and weaknesses of the neural-network algorithms discussed in class
determine under which circumstances neural networks are useful in real applications
distinguish between supervised and unsupervised learning and explain the key principles of the corresponding algorithms
efficiently and reliably implement the algorithms introduced in class on a computer, interpret the results of computer simulations
describe principles of more general optimisation algorithms
write well-structured technical reports in English presenting and explaining analytical calculations and numerical results
communicate results and conclusions in a clear and logical fashion

Content

Introduction to neural networks (McCulloch Pitts neurons, associative memory problem, Hopfield model and Hebb s rule, storage capacity, energy function)
Stochastic neural networks (noise, order parameter, mean-field theory for the storage capacity)
Optimisation
Supervised learning: perceptrons and layered networks (feed-forward networks, multilayer perceptrons, gradient descent, backpropagation, conjugate-gradient methods, performance of multilayer networks)
Unsupervised learning (Hebbian learning, Oja s rule, competitive learning, topographic maps)
Recurrent networks and time-series analysis (recurrent backpropagation, backpropagation in time
Reinforcement learning

Organisation

Lectures, set homework problems, examples classes.
Web-based course evaluation.

Literature

Lecture notes will be made available. They are based on the course book: Hertz, A. Krogh, and R. G. Palmer, Introduction to the theory of neural computation, Addison-Wesely, Redwood City (1991).

Additional reading: S. Haykin, Neural Networks: a comprehensive foundation, 2nd ed., Prentice Hall, New Jersey (1999)

Examination

The examination is based on exercises and homework assignments (100%). The examinator must be informed within a week after the course starts if a student would like to receive ECTS grades.


Published: Fri 18 Dec 2009. Modified: Mon 28 Nov 2016