Morten Hjorth-Jensen
446 lines
27 KiB
HTML
446 lines
27 KiB
HTML
<!--
|
|
Automatically generated HTML file from DocOnce source
|
|
(https://github.com/doconce/doconce/)
|
|
-->
|
|
<html>
|
|
<head>
|
|
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
|
|
<meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
|
<meta name="description" content="Week 35: Introduction to the course, Logistics and Practicalities">
|
|
|
|
<title>Week 35: Introduction to the course, Logistics and Practicalities</title>
|
|
|
|
<!-- Bootstrap style: bootstrap -->
|
|
<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
|
|
<!-- not necessary
|
|
<link href="https://netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" rel="stylesheet">
|
|
-->
|
|
|
|
<style type="text/css">
|
|
|
|
/* Add scrollbar to dropdown menus in bootstrap navigation bar */
|
|
.dropdown-menu {
|
|
height: auto;
|
|
max-height: 400px;
|
|
overflow-x: hidden;
|
|
}
|
|
|
|
/* Adds an invisible element before each target to offset for the navigation
|
|
bar */
|
|
.anchor::before {
|
|
content:"";
|
|
display:block;
|
|
height:50px; /* fixed header height for style bootstrap */
|
|
margin:-50px 0 0; /* negative fixed header height */
|
|
}
|
|
</style>
|
|
|
|
|
|
</head>
|
|
|
|
<!-- tocinfo
|
|
{'highest level': 2,
|
|
'sections': [('Overview of first week', 2, None, 'overview-of-first-week'),
|
|
('Reading Recommendations', 2, None, 'reading-recommendations'),
|
|
('Thursday August 26', 2, None, 'thursday-august-26'),
|
|
('Lectures and ComputerLab', 2, None, 'lectures-and-computerlab'),
|
|
('Announcement', 2, None, 'announcement'),
|
|
('Communication channels', 2, None, 'communication-channels'),
|
|
('Course Format', 2, None, 'course-format'),
|
|
('Teachers', 2, None, 'teachers'),
|
|
('Deadlines for projects (tentative)',
|
|
2,
|
|
None,
|
|
'deadlines-for-projects-tentative'),
|
|
('Recommended textbooks', 2, None, 'recommended-textbooks'),
|
|
('Prerequisites', 2, None, 'prerequisites'),
|
|
('Learning outcomes', 2, None, 'learning-outcomes'),
|
|
('Topics covered in this course: Statistical analysis and '
|
|
'optimization of data',
|
|
2,
|
|
None,
|
|
'topics-covered-in-this-course-statistical-analysis-and-optimization-of-data'),
|
|
('Topics covered in this course: Machine Learning',
|
|
2,
|
|
None,
|
|
'topics-covered-in-this-course-machine-learning'),
|
|
('Extremely useful tools, strongly recommended',
|
|
2,
|
|
None,
|
|
'extremely-useful-tools-strongly-recommended'),
|
|
('Other courses on Data science and Machine Learning at UiO',
|
|
2,
|
|
None,
|
|
'other-courses-on-data-science-and-machine-learning-at-uio'),
|
|
('Introduction', 2, None, 'introduction'),
|
|
('What is Machine Learning?',
|
|
2,
|
|
None,
|
|
'what-is-machine-learning'),
|
|
('Types of Machine Learning',
|
|
2,
|
|
None,
|
|
'types-of-machine-learning'),
|
|
('Essential elements of ML', 2, None, 'essential-elements-of-ml'),
|
|
('An optimization/minimization problem',
|
|
2,
|
|
None,
|
|
'an-optimization-minimization-problem'),
|
|
('A Frequentist approach to data analysis',
|
|
2,
|
|
None,
|
|
'a-frequentist-approach-to-data-analysis'),
|
|
('What is a good model?', 2, None, 'what-is-a-good-model'),
|
|
('What is a good model? Can we define it?',
|
|
2,
|
|
None,
|
|
'what-is-a-good-model-can-we-define-it'),
|
|
('Software and needed installations',
|
|
2,
|
|
None,
|
|
'software-and-needed-installations'),
|
|
('Python installers', 2, None, 'python-installers'),
|
|
('Useful Python libraries', 2, None, 'useful-python-libraries'),
|
|
('Installing R, C++, cython or Julia',
|
|
2,
|
|
None,
|
|
'installing-r-c-cython-or-julia'),
|
|
('Installing R, C++, cython, Numba etc',
|
|
2,
|
|
None,
|
|
'installing-r-c-cython-numba-etc'),
|
|
('Numpy examples and Important Matrix and vector handling '
|
|
'packages',
|
|
2,
|
|
None,
|
|
'numpy-examples-and-important-matrix-and-vector-handling-packages'),
|
|
('Basic Matrix Features', 2, None, 'basic-matrix-features'),
|
|
('Some famous Matrices', 3, None, 'some-famous-matrices'),
|
|
('More Basic Matrix Features',
|
|
3,
|
|
None,
|
|
'more-basic-matrix-features'),
|
|
('Numpy and arrays', 2, None, 'numpy-and-arrays'),
|
|
('Matrices in Python', 2, None, 'matrices-in-python'),
|
|
('Meet the Pandas', 2, None, 'meet-the-pandas'),
|
|
('Friday August 27', 2, None, 'friday-august-27'),
|
|
('Simple linear regression model using _scikit-learn_',
|
|
3,
|
|
None,
|
|
'simple-linear-regression-model-using-_scikit-learn_'),
|
|
('To our real data: nuclear binding energies. Brief reminder on '
|
|
'masses and binding energies',
|
|
3,
|
|
None,
|
|
'to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies'),
|
|
('Organizing our data', 3, None, 'organizing-our-data'),
|
|
('Seeing the wood for the trees',
|
|
3,
|
|
None,
|
|
'seeing-the-wood-for-the-trees'),
|
|
('And what about using neural networks?',
|
|
3,
|
|
None,
|
|
'and-what-about-using-neural-networks'),
|
|
('A first summary', 2, None, 'a-first-summary'),
|
|
('Why Linear Regression (aka Ordinary Least Squares and family)',
|
|
2,
|
|
None,
|
|
'why-linear-regression-aka-ordinary-least-squares-and-family'),
|
|
('Regression analysis, overarching aims',
|
|
2,
|
|
None,
|
|
'regression-analysis-overarching-aims'),
|
|
('Regression analysis, overarching aims II',
|
|
2,
|
|
None,
|
|
'regression-analysis-overarching-aims-ii'),
|
|
('Examples', 2, None, 'examples'),
|
|
('General linear models', 2, None, 'general-linear-models'),
|
|
('Rewriting the fitting procedure as a linear algebra problem',
|
|
2,
|
|
None,
|
|
'rewriting-the-fitting-procedure-as-a-linear-algebra-problem'),
|
|
('Rewriting the fitting procedure as a linear algebra problem, '
|
|
'more details',
|
|
2,
|
|
None,
|
|
'rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details'),
|
|
('Generalizing the fitting procedure as a linear algebra problem',
|
|
2,
|
|
None,
|
|
'generalizing-the-fitting-procedure-as-a-linear-algebra-problem'),
|
|
('Generalizing the fitting procedure as a linear algebra problem',
|
|
2,
|
|
None,
|
|
'generalizing-the-fitting-procedure-as-a-linear-algebra-problem'),
|
|
('Optimizing our parameters',
|
|
2,
|
|
None,
|
|
'optimizing-our-parameters'),
|
|
('Our model for the nuclear binding energies',
|
|
2,
|
|
None,
|
|
'our-model-for-the-nuclear-binding-energies'),
|
|
('Optimizing our parameters, more details',
|
|
2,
|
|
None,
|
|
'optimizing-our-parameters-more-details'),
|
|
('Interpretations and optimizing our parameters',
|
|
2,
|
|
None,
|
|
'interpretations-and-optimizing-our-parameters'),
|
|
('Interpretations and optimizing our parameters',
|
|
2,
|
|
None,
|
|
'interpretations-and-optimizing-our-parameters'),
|
|
('Some useful matrix and vector expressions',
|
|
2,
|
|
None,
|
|
'some-useful-matrix-and-vector-expressions'),
|
|
('Interpretations and optimizing our parameters',
|
|
2,
|
|
None,
|
|
'interpretations-and-optimizing-our-parameters'),
|
|
('Own code for Ordinary Least Squares',
|
|
2,
|
|
None,
|
|
'own-code-for-ordinary-least-squares'),
|
|
('Adding error analysis and training set up',
|
|
2,
|
|
None,
|
|
'adding-error-analysis-and-training-set-up'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('The $\\chi^2$ function', 2, None, 'the-chi-2-function'),
|
|
('Fitting an Equation of State for Dense Nuclear Matter',
|
|
2,
|
|
None,
|
|
'fitting-an-equation-of-state-for-dense-nuclear-matter'),
|
|
('The code', 2, None, 'the-code'),
|
|
('Splitting our Data in Training and Test data',
|
|
2,
|
|
None,
|
|
'splitting-our-data-in-training-and-test-data'),
|
|
('Exercises for week 36', 2, None, 'exercises-for-week-36'),
|
|
('Exercise 1: Setting up various Python environments',
|
|
2,
|
|
None,
|
|
'exercise-1-setting-up-various-python-environments'),
|
|
('Exercise 2: making your own data and exploring scikit-learn',
|
|
2,
|
|
None,
|
|
'exercise-2-making-your-own-data-and-exploring-scikit-learn'),
|
|
('Exercise 3: Normalizing our data',
|
|
2,
|
|
None,
|
|
'exercise-3-normalizing-our-data')]}
|
|
end of tocinfo -->
|
|
|
|
<body>
|
|
|
|
|
|
|
|
<script type="text/x-mathjax-config">
|
|
MathJax.Hub.Config({
|
|
TeX: {
|
|
equationNumbers: { autoNumber: "none" },
|
|
extensions: ["AMSmath.js", "AMSsymbols.js", "autobold.js", "color.js"]
|
|
}
|
|
});
|
|
</script>
|
|
<script type="text/javascript" async
|
|
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
|
|
</script>
|
|
|
|
|
|
|
|
|
|
<!-- Bootstrap navigation bar -->
|
|
<div class="navbar navbar-default navbar-fixed-top">
|
|
<div class="navbar-header">
|
|
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
|
|
<span class="icon-bar"></span>
|
|
<span class="icon-bar"></span>
|
|
<span class="icon-bar"></span>
|
|
</button>
|
|
<a class="navbar-brand" href="week34-bs.html">Week 35: Introduction to the course, Logistics and Practicalities</a>
|
|
</div>
|
|
|
|
<div class="navbar-collapse collapse navbar-responsive-collapse">
|
|
<ul class="nav navbar-nav navbar-right">
|
|
<li class="dropdown">
|
|
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
|
<ul class="dropdown-menu">
|
|
<!-- navigation toc: --> <li><a href="._week34-bs001.html#overview-of-first-week" style="font-size: 80%;"><b>Overview of first week</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs002.html#reading-recommendations" style="font-size: 80%;"><b>Reading Recommendations</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs003.html#thursday-august-26" style="font-size: 80%;"><b>Thursday August 26</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs004.html#lectures-and-computerlab" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs005.html#announcement" style="font-size: 80%;"><b>Announcement</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs006.html#communication-channels" style="font-size: 80%;"><b>Communication channels</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs007.html#course-format" style="font-size: 80%;"><b>Course Format</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs008.html#teachers" style="font-size: 80%;"><b>Teachers</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs009.html#deadlines-for-projects-tentative" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs010.html#recommended-textbooks" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs011.html#prerequisites" style="font-size: 80%;"><b>Prerequisites</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs012.html#learning-outcomes" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs013.html#topics-covered-in-this-course-statistical-analysis-and-optimization-of-data" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs014.html#topics-covered-in-this-course-machine-learning" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs015.html#extremely-useful-tools-strongly-recommended" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs016.html#other-courses-on-data-science-and-machine-learning-at-uio" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs017.html#introduction" style="font-size: 80%;"><b>Introduction</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs018.html#what-is-machine-learning" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs019.html#types-of-machine-learning" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs020.html#essential-elements-of-ml" style="font-size: 80%;"><b>Essential elements of ML</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs021.html#an-optimization-minimization-problem" style="font-size: 80%;"><b>An optimization/minimization problem</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs022.html#a-frequentist-approach-to-data-analysis" style="font-size: 80%;"><b>A Frequentist approach to data analysis</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs023.html#what-is-a-good-model" style="font-size: 80%;"><b>What is a good model?</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="#what-is-a-good-model-can-we-define-it" style="font-size: 80%;"><b>What is a good model? Can we define it?</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs025.html#software-and-needed-installations" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs026.html#python-installers" style="font-size: 80%;"><b>Python installers</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs027.html#useful-python-libraries" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs028.html#installing-r-c-cython-or-julia" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs029.html#installing-r-c-cython-numba-etc" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs030.html#numpy-examples-and-important-matrix-and-vector-handling-packages" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs031.html#basic-matrix-features" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs032.html#some-famous-matrices" style="font-size: 80%;"> Some famous Matrices</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs033.html#more-basic-matrix-features" style="font-size: 80%;"> More Basic Matrix Features</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs034.html#numpy-and-arrays" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs035.html#matrices-in-python" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs036.html#meet-the-pandas" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs037.html#friday-august-27" style="font-size: 80%;"><b>Friday August 27</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#simple-linear-regression-model-using-_scikit-learn_" style="font-size: 80%;"> Simple linear regression model using <b>scikit-learn</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies" style="font-size: 80%;"> To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#organizing-our-data" style="font-size: 80%;"> Organizing our data</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#seeing-the-wood-for-the-trees" style="font-size: 80%;"> Seeing the wood for the trees</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#and-what-about-using-neural-networks" style="font-size: 80%;"> And what about using neural networks?</a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs038.html#a-first-summary" style="font-size: 80%;"><b>A first summary</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs039.html#why-linear-regression-aka-ordinary-least-squares-and-family" style="font-size: 80%;"><b>Why Linear Regression (aka Ordinary Least Squares and family)</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs040.html#regression-analysis-overarching-aims" style="font-size: 80%;"><b>Regression analysis, overarching aims</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs041.html#regression-analysis-overarching-aims-ii" style="font-size: 80%;"><b>Regression analysis, overarching aims II</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs042.html#examples" style="font-size: 80%;"><b>Examples</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs043.html#general-linear-models" style="font-size: 80%;"><b>General linear models</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs044.html#rewriting-the-fitting-procedure-as-a-linear-algebra-problem" style="font-size: 80%;"><b>Rewriting the fitting procedure as a linear algebra problem</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs045.html#rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details" style="font-size: 80%;"><b>Rewriting the fitting procedure as a linear algebra problem, more details</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs047.html#generalizing-the-fitting-procedure-as-a-linear-algebra-problem" style="font-size: 80%;"><b>Generalizing the fitting procedure as a linear algebra problem</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs047.html#generalizing-the-fitting-procedure-as-a-linear-algebra-problem" style="font-size: 80%;"><b>Generalizing the fitting procedure as a linear algebra problem</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs048.html#optimizing-our-parameters" style="font-size: 80%;"><b>Optimizing our parameters</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs049.html#our-model-for-the-nuclear-binding-energies" style="font-size: 80%;"><b>Our model for the nuclear binding energies</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs050.html#optimizing-our-parameters-more-details" style="font-size: 80%;"><b>Optimizing our parameters, more details</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs054.html#interpretations-and-optimizing-our-parameters" style="font-size: 80%;"><b>Interpretations and optimizing our parameters</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs054.html#interpretations-and-optimizing-our-parameters" style="font-size: 80%;"><b>Interpretations and optimizing our parameters</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs053.html#some-useful-matrix-and-vector-expressions" style="font-size: 80%;"><b>Some useful matrix and vector expressions</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs054.html#interpretations-and-optimizing-our-parameters" style="font-size: 80%;"><b>Interpretations and optimizing our parameters</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs055.html#own-code-for-ordinary-least-squares" style="font-size: 80%;"><b>Own code for Ordinary Least Squares</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs056.html#adding-error-analysis-and-training-set-up" style="font-size: 80%;"><b>Adding error analysis and training set up</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs062.html#the-chi-2-function" style="font-size: 80%;"><b>The \( \chi^2 \) function</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs063.html#fitting-an-equation-of-state-for-dense-nuclear-matter" style="font-size: 80%;"><b>Fitting an Equation of State for Dense Nuclear Matter</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs064.html#the-code" style="font-size: 80%;"><b>The code</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs065.html#splitting-our-data-in-training-and-test-data" style="font-size: 80%;"><b>Splitting our Data in Training and Test data</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs066.html#exercises-for-week-36" style="font-size: 80%;"><b>Exercises for week 36</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs066.html#exercise-1-setting-up-various-python-environments" style="font-size: 80%;"><b>Exercise 1: Setting up various Python environments</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs066.html#exercise-2-making-your-own-data-and-exploring-scikit-learn" style="font-size: 80%;"><b>Exercise 2: making your own data and exploring scikit-learn</b></a></li>
|
|
<!-- navigation toc: --> <li><a href="._week34-bs066.html#exercise-3-normalizing-our-data" style="font-size: 80%;"><b>Exercise 3: Normalizing our data</b></a></li>
|
|
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</div>
|
|
</div>
|
|
</div> <!-- end of navigation bar -->
|
|
|
|
<div class="container">
|
|
|
|
<p> </p><p> </p><p> </p> <!-- add vertical space -->
|
|
|
|
<a name="part0024"></a>
|
|
<!-- !split -->
|
|
|
|
<h2 id="what-is-a-good-model-can-we-define-it" class="anchor">What is a good model? Can we define it? </h2>
|
|
|
|
<p>
|
|
The central question is this: what leads us to say that a model is correct or
|
|
optimal for a given data set? To make the model inference problem well posed, i.e.,
|
|
to guarantee that there is a unique optimal model for the given data, we need to
|
|
impose additional assumptions or restrictions on the class of models considered. To
|
|
this end, we should not be looking for just any model that can describe the data.
|
|
Instead, we should look for a <b>model</b> \( M \) that is the best among a restricted class
|
|
of models. In addition, to make the model inference problem computationally
|
|
tractable, we need to specify how restricted the class of models needs to be. A
|
|
common strategy is to start
|
|
with the simplest possible class of models that is just necessary to describe the data
|
|
or solve the problem at hand. More precisely, the model class should be rich enough
|
|
to contain at least one model that can fit the data to a desired accuracy and yet be
|
|
restricted enough that it is relatively simple to find the best model for the given data.
|
|
|
|
<p>
|
|
Thus, the most popular strategy is to start from the
|
|
simplest class of models and increase the complexity of the models only when the
|
|
simpler models become inadequate. For instance, if we work with a regression problem to fit a set of sample points, one
|
|
may first try the simplest class of models, namely linear models, followed obviously by more complex models.
|
|
|
|
<p>
|
|
How to evaluate which model fits best the data is something we will come back to over and over again in these sets of lectures.
|
|
|
|
<p>
|
|
<p>
|
|
<!-- navigation buttons at the bottom of the page -->
|
|
<ul class="pagination">
|
|
<li><a href="._week34-bs023.html">«</a></li>
|
|
<li><a href="._week34-bs000.html">1</a></li>
|
|
<li><a href="">...</a></li>
|
|
<li><a href="._week34-bs016.html">17</a></li>
|
|
<li><a href="._week34-bs017.html">18</a></li>
|
|
<li><a href="._week34-bs018.html">19</a></li>
|
|
<li><a href="._week34-bs019.html">20</a></li>
|
|
<li><a href="._week34-bs020.html">21</a></li>
|
|
<li><a href="._week34-bs021.html">22</a></li>
|
|
<li><a href="._week34-bs022.html">23</a></li>
|
|
<li><a href="._week34-bs023.html">24</a></li>
|
|
<li class="active"><a href="._week34-bs024.html">25</a></li>
|
|
<li><a href="._week34-bs025.html">26</a></li>
|
|
<li><a href="._week34-bs026.html">27</a></li>
|
|
<li><a href="._week34-bs027.html">28</a></li>
|
|
<li><a href="._week34-bs028.html">29</a></li>
|
|
<li><a href="._week34-bs029.html">30</a></li>
|
|
<li><a href="._week34-bs030.html">31</a></li>
|
|
<li><a href="._week34-bs031.html">32</a></li>
|
|
<li><a href="._week34-bs032.html">33</a></li>
|
|
<li><a href="._week34-bs033.html">34</a></li>
|
|
<li><a href="">...</a></li>
|
|
<li><a href="._week34-bs066.html">67</a></li>
|
|
<li><a href="._week34-bs025.html">»</a></li>
|
|
</ul>
|
|
<!-- ------------------- end of main content --------------- -->
|
|
|
|
</div> <!-- end container -->
|
|
<!-- include javascript, jQuery *first* -->
|
|
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
|
|
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
|
|
|
|
<!-- Bootstrap footer
|
|
<footer>
|
|
<a href="https://..."><img width="250" align=right src="https://..."></a>
|
|
</footer>
|
|
-->
|
|
|
|
|
|
<center style="font-size:80%">
|
|
<!-- copyright only on the titlepage -->
|
|
</center>
|
|
|
|
|
|
</body>
|
|
</html>
|
|
|
|
|