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DTSTART;TZID=Europe/Stockholm:20220117T080000
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DTSTAMP:20260606T143739
CREATED:20211022T070851Z
LAST-MODIFIED:20211022T071315Z
UID:58943-1642406400-1642784400@www.scilifelab.se
SUMMARY:NBIS workshop in Neural Nets and Deep Learning
DESCRIPTION:National course open for PhD students\, postdocs\, researchers and other employees in need of Neural networks and Deep Learning skills within all Swedish universities. \n\n\n\nImportant dates\n\n\n\nApplication opens: 2021-11-01 \n\n\n\nApplication closes: 2021-12-05 \n\n\n\nConfirmation to accepted students:  2021-12-19 \n\n\n\nResponsible teachers:  Claudio Mirabello\, Bengt Sennblad \n\n\n\nIf you do not receive information according to the above dates please contact: edu.neural-nets-deep-learning@nbis.se \n\n\n\nCourse fee\n\n\n\nA course fee* of 2200 SEK will be invoiced to accepted participants. This includes lunches\, coffee and snacks. \n\n\n\n*Please note that NBIS cannot invoice individuals \n\n\n\nCourse content\n\n\n\nThis course will give an introduction to the concept of Neural Networks (NN) and Deep Learning. \n\n\n\nTopics covered will include: \n\n\n\nNN building blocks\, including concepts such as neurons\, activation functions\, loss functions\, gradient descent and back-propagationConvolutional Neural NetworksRecursive Neural NetworksAutoencodersBest practices when designing NNs\n\n\n\nLearning Outcomes\n\n\n\nUpon completion of this course\, you will be able to: \n\n\n\nDistinguish the concepts of “Artificial Intelligence”\, “Machine Learning”\, “Neural Networks”\, “Deep Learning”Distinguish between different types of learning (e.g. supervised\, unsupervised\, reinforcement) and recognise which applies to their own problemDistinguish between linear and non-linear approaches and recognise which is best suited for application to their own problemDescribe what a feed-forward neural network (FFNN) is\, along with its components (neurons\, layers\, weights\, bias\, activation functions\, cost functions)Explain how training of a FFNN works from a mathematical point of view (gradient descent\, learning rate\, backpropagation)Execute with pen and paper a few steps of training of a very simple FFNN modelTell the difference between a shallow and a deep networkExplain broadly how different NN architectures are wired and how they workImplement and apply the most appropriate architecture to a given problem/datasetAnalyze training curves and prediction outputs to evaluate if the training has been successfulDebug possible issues with the training and suggest changes to fix themExplain the difference between training\, validation and testingDefine what overfitting is from a mathematical point of view\, and what issues it causesIdentify what constitutes good practices of dataset design and how to avoid introducing information leakage or other biases when building their own datasets\n\n\n\n\n\n\n\n\nCOURSE INFORMATION\n\n\n\nAPPLICATION\n\n\n\n\n\n\n\n\nEntry requirements\n\n\n\nRequired for being able to follow the course and complete the computer exercises: \n\n\n\nFamiliarity with Unix/LinuxAbility to bring your own laptop with Python and Jupyter Notebooks  installed for the practical exercisesProgramming/scripting experience in Python (e.g. having attended the NBIS workshop in basic Python or equivalent)Basic experience of statistics and mathematics (e.g. having attended the NBIS workshop Introduction to Biostatistics and Machine Learning or equivalent)\n\n\n\nDesired \n\n\n\nYou have experience of working with Jupyter NotebooksYou have a necessity  to work with large datasets (e.g. thousands of samples)\n\n\n\nDue to limited space the course can accommodate a maximum of 25 participants. If we receive more applications\, participants will be selected based on several criteria. Selection criteria include correct entry requirements\, motivation to attend the course as well as gender and geographical balance.
URL:https://www.scilifelab.se/event/nbis-workshop-in-neural-nets-and-deep-learning/
LOCATION:Navet\, SciLifeLab Uppsala\, SciLifeLab Uppsala\, BMC C11\, Husargatan 3\, Uppsala\, 75237\, Sweden
CATEGORIES:Course
ORGANIZER;CN="NBIS - National Bioinformatics Infrastructure Sweden":MAILTO:education@nbis.se
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