added p3

doc/Projects/2020/Project3/pdf/Project3.tex

@@ -1,7 +1,7 @@
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+  \fancyfoot[C]{{\footnotesize \copyright\ 1999-2021, "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html". Released under CC Attribution-NonCommercial 4.0 license}}
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+  \fancyfoot[C]{{\footnotesize \copyright\ 1999-2021, "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html". Released under CC Attribution-NonCommercial 4.0 license}}
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@@ -103,7 +103,7 @@ final,                   % draft: marks overfull hboxes, figures with paths
 \begin{center}
 {\LARGE\bf
 \begin{spacing}{1.25}
-Project 3 on Machine Learning, deadline December 16
+Project 3 on Machine Learning, deadline December 17, 2021
 \end{spacing}
 }
 \end{center}
@@ -123,7 +123,7 @@ Project 3 on Machine Learning, deadline December 16
 
 % --- begin date ---
 \begin{center}
-Dec 11, 2020
+Nov 13, 2021
 \end{center}
 % --- end date ---
 
@@ -179,7 +179,6 @@ The first part deals with structuring and reading the data, much along the same
 \paragraph{Part b).}
 You need to include at least two central algorithms, or as an alternative explore methods from decisions tree to bagging, random forests and boosting. Explain the basics of the methods you have chosen to work with. This would be your theory part.
 
-
 \paragraph{Part c).}
 Then describe your algorithm and its implementation and tests you have performed.
 
@@ -257,7 +256,6 @@ for $\Delta x=1/10$, $\Delta x=1/100$ using  $\Delta t$ as dictated by the stabi
 Study the solutions at two time points $t_1$ and $t_2$ where $u(x,t_1)$ is smooth but still significantly curved
 and $u(x,t_2)$ is almost linear, close to the stationary state.
 
-
 \paragraph{Part c) Neural networks.}
 Study now the lecture notes on solving ODEs and PDEs with neural
 network and use either your own code from project 2 or the
@@ -266,7 +264,6 @@ part b).  Discuss your results and compare them with the standard
 explicit scheme. Include also the analytical solution and compare with
 that.
 
-
 \paragraph{Part d) Solving eigenvalue problems.}
 Follow the discussion in the work of Yi \emph{et al.} in the article from
 \href{{https://www.sciencedirect.com/science/article/pii/S0898122104901101}}{Computers and Mathematics with Applications 47, 1155 (2004)}, and
@@ -277,7 +274,6 @@ eigenvalues. Compare with the solution from numerical diagonalization with stand
 \paragraph{Part e).}
 Finally, present a critical assessment of the methods you have studied and discuss the potential for the solving differential equations and eigenvalue problems with machine learning methods. 
 
-
 \subsection*{Introduction to numerical projects}
 
 Here follows a brief recipe and recommendation on how to write a report for each
@@ -347,8 +343,6 @@ Finally,
 we encourage you to collaborate. Optimal working groups consist of 
 2-3 students. You can then hand in a common report. 
 
-
-
 \subsection*{Software and needed installations}
 
 If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages, 
@@ -404,9 +398,6 @@ encompass communities of developers in the thousands or more. And the number
 of code developers and contributors keeps increasing.
 
 
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