Neural Nets (596)
Type:  Lecture with Exercises 
ECTS:  6 points 
Lectures:  Fridays, 08:3012:00 
Venue:  BIN 2.A.01 
Lecturers:  Prof. Rolf Pfeifer 
Target audience:  Recommended for MSc students. The course is interdisciplinary; it is also targeted at students from other fields than computer science, e.g. economics, biology, natural sciences, and psychology. 
Language:  English 
Assessment:  Written exam. Date: Friday, 14.06.2013, 8:00  9:30am 
Assistants:  Nico Schmidt, Matthias Weyland 
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multilayer perceptrons), supportvector machines, echostate networks, nonsupervised networks (competitive, Kohonen, Hebb), recurrent networks (Hopfield, CTRNNs  continuoustime recurrent neural networks), spiking neural networks, spiketime dependent plasticity, applications. Special consideration will be given to neural networks embedded in adaptive systems having to interact with the real world, such as embodied systems (in particular robots). Cooperation of neural control, morphology, materials, and environment. Evolutionary approaches to designing autonomous systems; interaction of learning and evolution. Network theory applied to brain networks; motifs.
Additional case studies will be discussed to deepen the understanding of neural networks, e.g. Neural interfacing  coupling neural systems with technology (in particular robotic devices), neural imaging studies, Distributed Adaptive Control (DAC), neural gas and DRNNs  Dynamically Rearranging Neural Networks (neuromodulatorbased networks), neural network models of memory.
This is an elementary, interdisciplinary introduction to neural networks, suited not only for computer scientists, but also for economists, biologists, psychologists, etc.
If you wish, you can do the exercises in groups of two  please hand in only one task sheet. You can also do them on your own if you prefer.
Important
The page will be subjected to changes over time. Please stay uptodate by checking it periodically.
Final Exam
List of topics relevant for exam (PDF, 69 KB)
List of topics relevant for the final exam.
The final exam will be Friday, 14 June, 2013, from 8.00 to 9.30am in the lecture room 2.A.01.
In order to attend the final exam, you must achieve 50% out of the total possible points of all the exercises (Task sheets 14) together (not each).
It will not be an openbook exam, so you are not allowed to use your books and notes. However, you don't have to learn any formulas by heart, as we will provide you with a sheet containing all formulas (but you do have to know which one applies to which type of Neural Net). No laptops or cell phones.
Please bring along:
 simple pocket calculator (no cell phones or other advanced programmable devices)
 your student ID card (required)
We wish you a lot of success.
Date  Topic  Lecture  Exercises 

22 February  Introduction, Linear Algebra  For an intuitive, 50min introduction to artificial neural networks, please consult this video which was recorded in the context of the ShanghAI Lectures. All the points raised in this video will be taken up again and discussed in more detail later in the class. Please also consult the pdf of the slides for this lecture. Neural networks require relatively little prior knowledge in mathematics, just some linear algebra and a bit of elementary calculus. For those who are not confident about their linear algebra skills, we will provide an introductory tutorial  including a set of exercises during the first lecture on 22 February, starting approx at 10.00h. If you are confident that you already master basic linear algebra, you don't need to attend this tutorial. 

1 march  Supervised models  Perceptron, Adaline, deltarule  
8 March  No lecture  Special event: Robots on Tour www.robotsontour.com  
15 March  Supervised models  Backpropagation: examples, properties; Error surfaces, Momentum term, Other improvements; Nfold crossvalidation, VC dimension  (Due date: 12 April) 
22 March  Supervised models  Cascade correlation, Suport vector machines (SVMs)  
29 March  No lecture  
5 April  No lecture  
12 April  Supervised models  Cascade correlation, Suport vector machines (SVMs)  hand in Task Sheet 1 (Due date: 26 April) 
19 April  Recurrent neural networks  Hopfield nets, Stocastic Models, CTRNNs (Continuous Time Recurrent Neural Network)  
26 April  Hybrid models  Guest lecture: Naveen Kuppuswamy: Reservoir computing  hand in Task Sheet 2; Videos of Lectures by Andrew Ng on SVMs: 1, 2, 3 
3 May  Unsupervised models  Nico Schmidt on Hebbian Learning, PCA, Oja's rule, Sanger's rule  
10 May  Biologically more plausible models  Guest lecture: Pascal Kaufmann: Basic neurophysiology, Spiking neurons, Cyborgs, Lamprey experiment, Brain imaging 
Simulator (ZIP, 315 KB) 
17 May  Unsupervised models  Competitive learning, SOM, Kohonenalgorithm, Extended Kohonen map (robot arm), Adaptive light compass.  it is OK to hand in Task Sheet 3 on 17 May! 
24 May  Application of recurrent networks  Morphological Computation, Evolutionary Robotics, Coevolution of morphology and control  hand in Task Sheet 4 
31 May  Wrapup  Wrapup session, questions, final discussion  
14 Jun  Exam 
Recommended Literature:
 J. Hertz, A. Krogh, R. Palmer, "Introduction to the theory of neural computation", AddisonWesley Publishing Company. A "classic"; a bit mathematical, but sound, written by physicists. Recommended as a complement to the lecture script. It covers most but not all topics of the class (e.g. Support Vector Machines, spiking neurons, etc.).
 S. Haykin, "Neural Networks: A comprehensive foundation", Prentice Hall. Very comprehensive, covers most of the topics of the class. Can also be used as an introductory textbook and as a complement to the class. It also introduces quite a few topics that go beyond the class.
 N. Cristianini, J. ShaweTaylor, "An Introduction to Support Vector Machines and other kernelbased learning methods", Cambridge University Press. Nice introduction to kernelbased learning machines. Mainly for the mathematically minded student. Support Vector Machines will be covered in class and are included in the book by Haykin.
 R. Rojas, "Neural Networks  A Systematic Introduction", SpringerVerlag, 1996.
A nicely and comprehensively written overview of the field with robotics application examples.
The book can be downloaded for free here:
http://www.inf.fuberlin.de/inst/agki/rojas_home/pmwiki/pmwiki.php?n=Books.NeuralNetworksBook
Materials that are useful for the understanding of the course:
 The Neural Network script (PDF, 8 MB)
 Lecture slides (PDF, 488 KB) from Qian Zhao about Cascade Correlation
 Paper (PDF, 171 KB) about Cascade Correlation
 NeuralNetworks Matlab demo (ZIP, 3 MB)
 GreekAlphabet (PDF, 28 KB)
 Linear Algebra Collection of formulas (PDF, 20 KB)
 Java NNSimulator and Cascade Correlation applet (ZIP, 315 KB) (ZIP)
 NNSimulator Manual (PDF, 1 MB)
 ART (adaptive resonance theory) article by Gail A. Carpenter and Stephen Grossberg
 Support Vector Machines: Nice and short introduction to SVM. (PDF, 941 KB)
 What is a supprt vector machine? (PDF, 242 KB)
 SOM2D Demo  Python implementation (ZIP, 2 KB) (ZIP)  Simple code and visualization of 2D SOM.
 Tutorial on training recurrent neural networks (echo state network) (PDF, 1 MB)
 MLP demo: Recognition of handwritten digits (ZIP, 391 KB) (ZIPFile) (using Matlab Neural Network Toolbox)
 PCA demo (ZIP, 1 KB): use hebbian learning to find the principle components (Matlab script)
 PCA tutorial (PDF, 323 KB) by Jonathon Shlens
 Reservoir Computing Slides (PDF, 6 MB) by Naveen Kuppuswamy
 The Limits of Intelligence (PDF, 1 MB) by Douglas Fox
Links
Some links on the internet that are useful for the understanding of the course
 CascadeCorrelation Tutorial
 CascadeCorrelation Wikipedia
 pattern recognition applet in japanese
 multilayer perceptron applet
 Support Vector Machines video tutorial 1 by Colin Campbell
 Support Vector Machines video tutorial 2 by ChihJen Lin
 Support Vector Machine Java Applet 1
 Support Vector Machine Java Applet 2
 Pattern completion using a hopfield net
 Pattern recognition applet using a hopfield net
 Travelling Salesman Problem: using a hopfield network in order to find possible solutions
 SelfOrganizing Maps: Kohonen Network
 Travelling Salesman Problem: With a Kohonen network you can get quite satisfying results
 SelfOrganizing Maps: Kohonen Network in 3D
 NERO: Neuro Evolving Robotics Operatives
 SVM Toy, from National Taiwan University