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updating project 2

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mhjensen
2020-10-06 22:32:36 +02:00
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doc/src/Projects/2020/Project2/Project2.do.txt
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TITLE: Project 2 on Machine Learning, deadline November 8
TITLE: Project 2 on Machine Learning, deadline November 7
AUTHOR: "Data Analysis and Machine Learning FYS-STK3155/FYS4155":"http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html" {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo, Norway
DATE: today
@@ -6,9 +6,9 @@ DATE: today
===== Classification and Regression, from linear and logistic regression to neural networks =====
The main aim of this project is to study both classification and
regression problems, where we can reuse the regression algorithms studied
regression problems by developing our own feed-forward neural network (FFNN) code. We can reuse the regression algorithms studied
in project 1. We will include logistic regresion for classification
problems and write our own multilayer perceptron code for studying
problems and write our own FFNNcode for studying
both regression and classification problems. The codes developed in
project 1, including bootstrap and/or cross-validation as well as the
computation of the mean-squared error and/or the $R2$ or the accuracy score (classification problems) functions can
@@ -19,9 +19,7 @@ The data sets that we propose here are (the default sets)
* Regression (fitting a continuous function). In this part you will need to bring up your results from project 1 and compare these with what you get from you Neural Network code to be developed here. The data sets could be
o Either the Franke function or the terrain data from project 1, or data sets your propose.
* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are
o The credit card data set from "UCI":"https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients". This data set links to a "recent scientific article":"https://bradzzz.gitbooks.io/ga-seattle-dsi/content/dsi/dsi_05_classification_databases/2.1-lesson/assets/datasets/DefaultCreditCardClients_yeh_2009.pdf". Furthermore, in the lecture slides on "Logistic Regression":"https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html", you will find an example code for the Credit Card data. You could use this code as an example on how to read the data and use _Scikit-Learn_ to run a classification problem.
* Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called MNIST data set of images representing hand-written numbers from zero to nine.
However, if you would like to study other data sets, feel free to
propose other sets. What we listed here are mere suggestions from our
@@ -180,20 +178,16 @@ project.
===== Format for electronic delivery of report and programs =====
The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:
* Use Devilry to hand in your projects, log in at URL:"http://devilry.ifi.uio.no" with your normal UiO username and password and choose either 'fysstk3155' or 'fysstk4155'. There you can load up the files within the deadline.
* Use Canvas to hand in your projects, log in at URL:"https://www.uio.no/english/services/it/education/canvas/" with your normal UiO username and password.
* Upload _only_ the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.
* In your git repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.
* In this and all later projects, you should include tests (for example unit tests) of your code(s).
* Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.
* Upload _only_ the report file or the link to your GitHub/GitLab or similar typo of repos! For the source code file(s) you have developed please provide us with your link to your GitHub/GitLab or similar domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them.
* In your GitHub/GitLab or similar repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.
Finally,
@@ -237,23 +231,3 @@ of code developers and contributors keeps increasing.
o For project 3, you should feel free to use your own codes from projects 1 and 2, eventually write your own for SVMs and/or Decision trees and random forests' or use the available functionality of _scikit-learn_, _tensorflow_, etc.
o The estimates you used and tested in projects 1 and 2 should also be included, that is the $R2$-score, _MSE_, cross-validation and/or bootstrap if these are relevant.
o If possible, you should link the data sets with exisiting research and analyses thereof. Scientific articles which have used Machine Learning algorithms to analyze the data are highly welcome. Perhaps you can improve previous analyses and even publish a new article?
o A critical assessment of the methods with ditto perspectives and recommendations is also something you need to include.