Neural Networks (for AI)
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  KIB.NNKI03 
Vaknaam  Neural Networks (for AI) 
Niveau(s)  bachelor 
Voertaal  Engels 
Periode  semester II b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Neural Networks (for AI)  
Leerdoelen  At the end of this course, the student:  does not identify 'neural networks' with 'deep learning', but is instead aware of the interdisciplinary diversity of neural network modeling approaches in the wider neural, cognitive and information processing sciences  can practically use feedforward neural networks in machine learning applications  understands elementary mathematical formalism to capture dynamical and stochastic phenomena in recurrent neural networks  has an orientation in the wider research landscape of NNs which enables him/her to approach research fields adjacent to AI / machine learning, in particular computational neuroscience, theoretical physics / complex systems and neuromorphic microchip technologies. 

Omschrijving  'Neural Networks' (NN) is an umbrella term for models of distributed, parallel information processing. In all neural network models, a (large) number of 'neurons' interact by spreading out activation patterns along 'synaptic' connections which can be shaped by learning mechanisms. This general scheme has been worked out into an enormous variety of concrete models in a diversity of scientific disciplines and application scenarios. This course gives an introduction to a choice of NN specimen which together illustrate the powers of NN models as an integrative link between AI, machine learning, computational neuroscience, theoretical physics and – since a few years increasing in importance – microchip technologies and material science. Concretely, the following topics will be treated:  A rehearsal of machine learning basics: overfitting, regularization, crossvalidation  Perceptrons (classical, multilayer), and a glance at deep feedforward networks, such as CNNs and autoencoders  An analysis of computational challenges inherent to backpropagation  A crash course on dynamical systems  to aid with the topic of Recurrent Neural Networks  LSTM and other gated networks for "deep" learning of temporal data; backpropagation through time algorithm  Hopfield networks – the classical NN model of neural memory and pattern completion  Boltzmann machines and restricted Boltzmann machines  Abstract 'NNrelated' models of parallel information processing and selforganization: spin glass systems, Markov random fields, sampling algorithms  A selection of (R)NN topics from computational neuroscience (to be decided), for instance cortical microcircuits, Bayesian information processing in spiking RNNs, neural motor control architectures, selforganizing maps, selforganized criticality or other (self)regulatory mechanisms for neural dynamics, dendritic information processing  Reservoir computing  An introduction to 'neuromorphic computing', a field between NNs, microchip fabrication and material science, of particular interest in Groningen, as the core research theme of our CogniGron research center 

Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC), Werkcollege (T)
(The practical work will be a practical miniproject, in parallel with the lecture, where a neural network presented in the course is implemented and applied to a task suitable for that kind of network. The deliverable is a final report. Weekly homeworks are for exercise only (nongraded).) 

Toetsvorm 
Schriftelijk tentamen (WE), Verslag (R)
(Final Mark: The examination will count for 50% of the final mark and the final practical report also for 50%. The grade of the exam needs to be higher or equal to 5.0 in order to pass this course.) 

Vaksoort  bachelor  
Coördinator  prof. dr. H. Jaeger  
Docent(en)  prof. dr. H. Jaeger  
Verplichte literatuur 


Entreevoorwaarden  Obligatory: Linear Algebra & Multivariable Calculus, Calculus for AI. Students for whom this course is not mandatory may be submitted to an admission test checking the mathematics background.  
Opmerkingen  
Opgenomen in 
