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Kursplan för

Läsår
FFR135 - Artificial neural networks
 
Kursplanen fastställd 2012-02-22 av programansvarig (eller motsvarande)
Ägare: MPCAS
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Bioteknik, Kemiteknik, Teknisk fysik
Institution: 16 - FYSIK


Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: B

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0100 Inlämningsuppgift 7,5 hp Betygskala: TH   7,5 hp    

I program

MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Årskurs 2 (valbar)
TKITE INFORMATIONSTEKNIK, CIVILINGENJÖR, Årskurs 3 (obligatoriskt valbar)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Årskurs 1 (obligatorisk)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Årskurs 2 (valbar)

Examinator:

Professor  Bernhard Mehlig



Behörighet:

För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

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

Syfte

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

Lärandemål (efter fullgjord kurs ska studenten kunna)

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

Innehåll

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

Course home page

Organisation

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

Litteratur

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.


Sidansvarig Publicerad: on 24 jan 2018.