mhjensen
413 lines
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413 lines
15 KiB
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('Additional courses of interest', 2, None, '___sec16'),
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('Best wishes to you all and thanks so much for your heroic '
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<!-- ------------------- main content ---------------------- -->
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<center><h1>Summary of course</h1></center> <!-- document title -->
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<p>
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<!-- author(s): Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no -->
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<center>
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<b>Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no</b> [1, 2]
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</center>
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<p>
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<!-- institution(s) -->
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<center>[1] <b>Department of Physics and Center of Mathematics for Applications, University of Oslo</b></center>
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<center>[2] <b>National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p>
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<center><h4>Nov 27, 2019</h4></center> <!-- date -->
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<br>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec0">What? Me worry? No final exam in this course! </h2>
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<br /><br /><center><p><img src="figures/exam1.jpeg" align="bottom" width=500></p></center><br /><br />
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<br /><br /><center><p><img src="figures/whatmeworry.jpeg" align="bottom" width=500></p></center><br /><br />
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec1">What did I learn in school this year? </h2>
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<p>
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<a href="http://hplgit.github.io/edu/py_vs_m/computing_competence.html" target="_blank">Our ideal about knowledge on computational science</a>
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<p>
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Does that match the experiences you have made this semester?
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<br /><br /><center><p><img src="figures/exam2.jpg" align="bottom" width=500></p></center><br /><br />
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec2">Topics we have covered this year </h2>
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<p>
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The course has two central parts
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<ol>
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<li> Statistical analysis and optimization of data</li>
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<li> Machine learning</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec3">Statistical analysis and optimization of data </h2>
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<p>
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The following topics will be covered
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<ol>
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<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
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<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
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<li> Central elements of Bayesian statistics and modeling;</li>
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<li> Central elements from linear algebra</li>
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<li> Gradient methods for data optimization</li>
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<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;</li>
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<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data.</li>
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<li> Principal Component Analysis.</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec4">Machine learning </h2>
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<p>
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The following topics will be covered
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<ol>
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<li> Linear methods for regression and classification;</li>
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<li> Neural networks;</li>
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<li> Decisions trees, random forests, boosting and bagging</li>
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<li> Support vector machines</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec5">Learning outcomes and overarching aims of this course </h2>
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<p>
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The course introduces a variety of central algorithms and methods
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essential for studies of data analysis and machine learning. The
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course is project based and through the various projects, normally
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three, you will be exposed to fundamental research problems
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in these fields, with the aim to reproduce state of the art scientific
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results. The students will learn to develop and structure large codes
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for studying these systems, get acquainted with computing facilities
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and learn to handle large scientific projects. A good scientific and
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ethical conduct is emphasized throughout the course.
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<ul>
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<li> Understand linear methods for regression and classification;</li>
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<li> Learn about neural network;</li>
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<li> Learn about baggin, boosting and trees</li>
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<li> Support vector machines</li>
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<li> Learn about basic data analysis;</li>
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<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
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<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
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<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.</li>
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</ul>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec6">Perspective on Machine Learning </h2>
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<ol>
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<li> Rapidly emerging application area</li>
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<li> Experiment AND theory are evolving in many many fields. Still many low-hanging fruits.</li>
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<li> Requires education/retraining for more widespread adoption</li>
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<li> A lot of “word-of-mouth” development methods</li>
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</ol>
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Huge amounts of data sets require automation, classical analysis tools often inadequate.
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High energy physics hit this wall in the 90’s.
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In 2009 single top quark production was determined via <a href="https://arxiv.org/pdf/0903.0850.pdf" target="_blank">Boosted decision trees, Bayesian
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Neural Networks, etc.</a>
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec7">Machine Learning Research </h2>
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<p>
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Where to find recent results:
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<ol>
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<li> Conference proceedings, arXiv and blog posts!</li>
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<li> <b>NIPS</b>: <a href="https://papers.nips.cc" target="_blank">Neural Information Processing Systems</a></li>
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<li> <b>ICLR</b>: <a href="https://openreview.net/group?id=ICLR.cc/2018/Conference#accepted-oral-papers" target="_blank">International Conference on Learning Representations</a></li>
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<li> <b>ICML</b>: International Conference on Machine Learning</li>
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<li> <a href="http://www.jmlr.org/papers/v19/" target="_blank">Journal of Machine Learning Research</a></li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec8">Hot Topics Now </h2>
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<ol>
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<li> Boosting techniques and complex neural networks</li>
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<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
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<li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
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<li> Transfer learning</li>
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<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec9">Starting your Machine Learning Project </h2>
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<ol>
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<li> Identify problem type: classification, generation, regression</li>
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<li> Consider your data carefully</li>
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<li> Choose a simple model that fits 1. and 2.</li>
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<li> Consider your data carefully again… data representation</li>
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<li> Based on results, feedback loop to earliest possible point</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec10">Choose a Model and Algorithm </h2>
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<ol>
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<li> Supervised?</li>
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<li> Start with the simplest model that fits your problem</li>
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<li> Start with minimal processing of data</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec11">Preparing Your Data </h2>
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<ol>
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<li> Shuffle your data</li>
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<li> Mean center your data</li>
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<ul>
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<li> Why?</li>
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</ul>
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<li> Normalize the variance</li>
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<ul>
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<li> Why?</li>
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</ul>
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<li> <b>Whitening</b></li>
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<ul>
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<li> Decorrelates data</li>
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<li> Can be hit or miss</li>
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</ul>
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<li> When to do train/test split?</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec12">Which Activation and Weights to Choose in Neural Networks </h2>
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<ol>
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<li> RELU? ELU?</li>
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<li> Sigmoid or Tanh?</li>
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<li> Set all weights to 0?</li>
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<ul>
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<li> Terrible idea</li>
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</ul>
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<li> Set all weights to random values?</li>
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<ul>
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<li> Small random values</li>
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</ul>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec13">Optimization Methods and Hyperparameters </h2>
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<ol>
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<li> Stochastic gradient descent
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<ol type="a"></li>
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<li> Stochastic gradient descent + momentum</li>
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</ol>
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<li> State-of-the-art approaches:</li>
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<ul>
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<li> RMSProp</li>
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<li> Adam</li>
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</ul>
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</ol>
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Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifiers, depths of trees and many other. Need to explore a large set of hyperparameters and regularization methods.
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<p>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec14">Resampling </h2>
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<p>
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When do we resample?
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<ol>
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<li> Bootstrap</li>
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<li> Cross-validation</li>
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<li> Jackknife and many other</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec15">Other courses on Data science and Machine Learning at UiO </h2>
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<p>
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The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
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<ol>
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<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
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<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_blank">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 – Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
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<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 – Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 – Dyp læring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec16">Additional courses of interest </h2>
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<ol>
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<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_blank">STK4051 Computational Statistics</a></li>
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<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_blank">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
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</ol>
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<!-- !split --><br><br><br><br><br><br><br><br><br><br>
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<h2 id="___sec17">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
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<p>
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<br /><br /><center><p><img src="figures/Nebbdyr2.png" align="bottom" width=500></p></center><br /><br />
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<p>
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<!-- ------------------- end of main content --------------- -->
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<center style="font-size:80%">
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<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no. Released under CC Attribution-NonCommercial 4.0 license
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</center>
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</body>
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