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<center><h1>Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
</center>
<p>
<!-- institution(s) -->
<center>[1] <b>Department of Physics, University of Oslo</b></center>
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec0">Overview of first week </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
<li> Friday August 23: Linear regression</li>
<li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
</ul>
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<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec1">Lectures and ComputerLab </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
<li> Weekly reading assignments needed to solve projects and exercises.</li>
<li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
<li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
<li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
<li> Weekly plans and all other information are on the official webpage.</li>
<li> No final exam, three projects that are graded and have to be approved.</li>
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<h2 id="___sec2">Course Format </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> Three compulsory projects. Electronic reports only using <a href="https://devilry.ifi.uio.no/" target="_blank">devilry</a> to hand in projects and <a href="https://github.com/" target="_blank">Git</a> for repository and all your material.</li>
<li> Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
<ol type="a"></li>
<li> For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.</li>
<li> If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course</li>
<li> Poster session where all participants can study and discuss the other proposals.</li>
<li> Based on feedback etc, each group finalizes the report and submits for grading.</li>
</ol>
<li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programmin languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_blank">github address</a> of the course.</li>
</ul>
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<h2 id="___sec3">Teachers and ComputerLab </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<p>
<b>Teachers :</b>
<ol>
<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
<li> <a href="https://github.com/Schoyen" target="_blank">&#216;yvind Sigmundson Sch&#248;yen</a></li>
</ol>
<table border="1">
<thead>
<tr><th align="center"> day </th> <th align="center"> Time </th> </tr>
</thead>
<tbody>
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
</tbody>
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</div>
<p>
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<h2 id="___sec4">Deadlines for projects (tentative) </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ol>
<li> Project 1: September 30 (graded with feedback)</li>
<li> Project 2: November 13 (graded with feedback)</li>
<li> Project 3: December 15 (graded with feedback)</li>
</ol>
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec5">Learning outcomes </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
<li> Be capable of extending the acquired knowledge to other systems and cases</li>
<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
<li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
<li> Learn about about decision trees and random forests</li>
<li> Learn about support vector machines and kernel transformations</li>
<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
<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>
</ul>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<ul>
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
<li> Central elements of Bayesian statistics and modeling</li>
<li> Gradient methods for data optimization</li>
<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
<li> Linear methods for regression and classification</li>
<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
</ul>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
The following topics will be covered
<ul>
<li> Linear Regression and Logistic Regression</li>
<li> Neural networks and deep learning</li>
<li> Decisions trees and nearest neighbor algorithms</li>
<li> Support vector machines</li>
<li> Bayesian Neural Networks</li>
<li> Boltzmann Machines</li>
<li> Dimensionality reduction, from PCA to cluster models</li>
</ul>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b>and discussed at the lab sessions.</b>
<p>
<ul>
<li> GIT for version control, highly recommended</li>
<li> Devilry for handing in projects, next week</li>
<li> Anaconda and other Python environments, see intro slides</li>
</ul>
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec9">Other courses on Data science and Machine Learning at UiO </h2>
<p>
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.
<ol>
<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>
<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>
<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>
<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>
<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>
<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>
<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>
<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>
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 Deep learning for autonomous systems</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>
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_blank">STK4051 Computational Statistics</a></li>
<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>
</ol>
<!-- ------------------- end of main content --------------- -->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>
</body>
</html>
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