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update on intro

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mhjensen
2020-08-19 11:44:39 +02:00
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doc/pub/Intro2Course/html/Intro2Course.html
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@@ -69,26 +69,27 @@ div { text-align: justify; text-justify: inter-word; }
'sections': [('Overview of first week', 2, None, '___sec0'),
('Lectures and ComputerLab', 2, None, '___sec1'),
('Course Format', 2, None, '___sec2'),
('Teachers and ComputerLab', 2, None, '___sec3'),
('Teachers', 2, None, '___sec3'),
('Deadlines for projects (tentative)', 2, None, '___sec4'),
('Learning outcomes', 2, None, '___sec5'),
('Prerequisites', 2, None, '___sec5'),
('Learning outcomes', 2, None, '___sec6'),
('Topics covered in this course: Statistical analysis and '
'optimization of data',
2,
None,
'___sec6'),
'___sec7'),
('Topics covered in this course: Machine Learning',
2,
None,
'___sec7'),
'___sec8'),
('Extremely useful tools, strongly recommended',
2,
None,
'___sec8'),
'___sec9'),
('Other courses on Data science and Machine Learning at UiO',
2,
None,
'___sec9')]}
'___sec10')]}
end of tocinfo -->
<body>
@@ -114,7 +115,7 @@ end of tocinfo -->
<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 -->
<center><h4>Aug 19, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -127,10 +128,10 @@ end of tocinfo -->
<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>
<li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
<li> Friday August 21: Linear regression</li>
<li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li>
</ul>
</div>
@@ -146,12 +147,10 @@ end of tocinfo -->
<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> Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
<li> Weekly reading assignments and videos 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>
</ul>
@@ -187,7 +186,7 @@ end of tocinfo -->
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec3">Teachers and ComputerLab </h2>
<h2 id="___sec3">Teachers </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -197,26 +196,26 @@ end of tocinfo -->
<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>
<ul>
<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
<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>
</table>
<ul>
<li> <b>Phone</b>: +47-48257387</li>
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;470</li>
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
</ul>
<li> &#216;yvind Sigmundson Sch&#248;yen, oyvinssc@student.matnat.uio.no</li>
<ul>
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room F&#216;452</li>
</ul>
<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
<li> Per-Dimitri S&#248;nsteland, perdimitri.bs@gmail.com</li>
</ul>
</div>
@@ -231,12 +230,12 @@ end of tocinfo -->
<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>
<li> Project 1: September 28 (graded with feedback)</li>
<li> Project 2: November 2 (graded with feedback)</li>
<li> Project 3: December 7 (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.
Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
</div>
@@ -245,24 +244,43 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec5">Learning outcomes </h2>
<h2 id="___sec5">Prerequisites </h2>
<p>
Basic knowledge in programming and mathematics, with an emphasis on
linear algebra. Knowledge of Python or/and C++ as programming
languages is strongly recommended and experience with Jupiter notebook
is recommended. Required courses are the equivalents to the University
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
of the corresponding computing and programming courses INF1000/INF1110
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
offer nowadays a basic programming course (often compulsory) where
Python is the recurring programming language.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Learning outcomes </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<p>
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
<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>
<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> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
<li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
<li> Learn about support vector machines and kernel transformations;</li>
<li> Reduction of data sets, from PCA to clustering;</li>
<li> Autoencoders and Reinforcement Learning;</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++ and/or Fortran (Fortran2003 or later).</li>
</ul>
</div>
@@ -270,22 +288,34 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
<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>
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
<p>
The course has two central parts
<ol>
<li> Statistical analysis and optimization of data</li>
<li> Machine learning</li>
</ol>
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<b>Statistical analysis and optimization of data.</b>
<p>
<p>
The following topics will be covered
<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>
<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, Gibbs sampling and Metropolis-Hastings sampling;</li>
<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
</ul>
</div>
@@ -293,7 +323,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -302,21 +332,25 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
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> Linear Regression and Logistic Regression;</li>
<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
<li> Decisions trees, Random Forests, Bagging and Boosting</li>
<li> Support vector machines</li>
<li> Bayesian Neural Networks</li>
<li> Bayesian linear and logistic regression</li>
<li> Boltzmann Machines</li>
<li> Dimensionality reduction, from PCA to cluster models</li>
<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
</ul>
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
</div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -324,9 +358,8 @@ The following topics will be covered
<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>
<li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
<li> Anaconda and other Python environments, see intro slides and first exercise session</li>
</ul>
</div>
@@ -334,7 +367,7 @@ The following topics will be covered
<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>
<h2 id="___sec10">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.
@@ -358,7 +391,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
<!-- copyright --> &copy; 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
</center>