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small update on textbook

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mhjensen
2020-08-19 13:08:42 +02:00
parent 775ad73418
commit 75835e62fd
19 changed files with 332 additions and 273 deletions
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doc/pub/Intro2Course/html/._Intro2Course-bs000.html
+15 -13
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
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@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -155,7 +157,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs008.html">9</a></li>
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs001.html
+15 -13
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
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<body>
@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -146,7 +148,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="">...</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs002.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs002.html
+16 -12
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
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<body>
@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -148,6 +150,8 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="">...</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs003.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs003.html
+15 -12
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
('Course Format', 2, None, '___sec2'),
('Teachers', 2, None, '___sec3'),
('Deadlines for projects (tentative)', 2, None, '___sec4'),
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<body>
@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -153,6 +155,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs004.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs004.html
+15 -12
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
('Course Format', 2, None, '___sec2'),
('Teachers', 2, None, '___sec3'),
('Deadlines for projects (tentative)', 2, None, '___sec4'),
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'___sec9'),
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('Other courses on Data science and Machine Learning at UiO',
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'___sec11')]}
end of tocinfo -->
<body>
@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -163,6 +165,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs005.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs005.html
+15 -12
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
('Course Format', 2, None, '___sec2'),
('Teachers', 2, None, '___sec3'),
('Deadlines for projects (tentative)', 2, None, '___sec4'),
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('Recommended textbooks', 2, None, '___sec5'),
('Prerequisites', 2, None, '___sec6'),
('Learning outcomes', 2, None, '___sec7'),
('Topics covered in this course: Statistical analysis and '
'optimization of data',
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'___sec7'),
'___sec8'),
('Topics covered in this course: Machine Learning',
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'___sec8'),
'___sec9'),
('Extremely useful tools, strongly recommended',
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'___sec9'),
'___sec10'),
('Other courses on Data science and Machine Learning at UiO',
2,
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'___sec10')]}
'___sec11')]}
end of tocinfo -->
<body>
@@ -91,12 +92,13 @@ end of tocinfo -->
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -149,6 +151,7 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs006.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs006.html
+20 -24
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,20 +114,13 @@ end of tocinfo -->
<a name="part0006"></a>
<!-- !split -->
<h2 id="___sec5" class="anchor">Prerequisites </h2>
<h2 id="___sec5" class="anchor">Recommended textbooks </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.
<ul>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_self">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a></li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_self">Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition</a></li>
</ul>
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
@@ -142,6 +137,7 @@ Python is the recurring programming language.
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs007.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs007.html
+25 -35
View File
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,31 +114,18 @@ end of tocinfo -->
<a name="part0007"></a>
<!-- !split -->
<h2 id="___sec6" class="anchor">Learning outcomes </h2>
<h2 id="___sec6" class="anchor">Prerequisites </h2>
<p>
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<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> 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>
</div>
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>
<p>
@@ -155,6 +144,7 @@ This course aims at giving you insights and knowledge about many of the central
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs008.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs008.html
+27 -31
View File
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,17 +114,7 @@ end of tocinfo -->
<a name="part0008"></a>
<!-- !split -->
<h2 id="___sec7" class="anchor">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
<h2 id="___sec7" class="anchor">Learning outcomes </h2>
<p>
<div class="panel panel-default">
@@ -130,16 +122,19 @@ These topics will be scattered thorughout the course and may not necessarily be
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>
The following topics will be covered
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> 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>
<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>
</div>
@@ -162,6 +157,7 @@ The following topics will be covered
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs009.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs009.html
+35 -24
View File
@@ -46,25 +46,26 @@ Automatically generated HTML file from DocOnce source
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,27 +114,35 @@ end of tocinfo -->
<a name="part0009"></a>
<!-- !split -->
<h2 id="___sec8" class="anchor">Topics covered in this course: Machine Learning </h2>
<h2 id="___sec8" class="anchor">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="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>
The following topics will be covered
<ul>
<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 linear and logistic regression</li>
<li> Boltzmann Machines</li>
<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</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>
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
<p>
</div>
</div>
@@ -154,6 +164,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
<li class="active"><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs010.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/._Intro2Course-bs010.html
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,17 +114,27 @@ end of tocinfo -->
<a name="part0010"></a>
<!-- !split -->
<h2 id="___sec9" class="anchor">Extremely useful tools, strongly recommended </h2>
<h2 id="___sec9" class="anchor">Topics covered in this course: Machine Learning </h2>
<p>
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
The following topics will be covered
<ul>
<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>
<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 linear and logistic regression</li>
<li> Boltzmann Machines</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.
<p>
</div>
</div>
@@ -144,6 +156,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li class="active"><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs011.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -112,26 +114,22 @@ end of tocinfo -->
<a name="part0011"></a>
<!-- !split -->
<h2 id="___sec10" class="anchor">Other courses on Data science and Machine Learning at UiO </h2>
<h2 id="___sec10" class="anchor">Extremely useful tools, strongly recommended </h2>
<p>
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><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.
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ol>
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">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="_self">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="_self">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="_self">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="_self">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="_self">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="_self">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="_self">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="_self">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="_self">STK4051 Computational Statistics</a></li>
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
</ol>
<ul>
<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>
</div>
<p>
<p>
<!-- navigation buttons at the bottom of the page -->
<ul class="pagination">
@@ -147,6 +145,8 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li class="active"><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs012.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/Intro2Course-bs.html
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Recommended textbooks</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Prerequisites</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs012.html#___sec11" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
@@ -155,7 +157,7 @@ end of tocinfo -->
<li><a href="._Intro2Course-bs008.html">9</a></li>
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._Intro2Course-bs011.html">12</a></li>
<li><a href="._Intro2Course-bs012.html">13</a></li>
<li><a href="._Intro2Course-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/Intro2Course/html/Intro2Course-reveal.html
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@@ -270,7 +270,17 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<section>
<h2 id="___sec5">Prerequisites </h2>
<h2 id="___sec5">Recommended textbooks </h2>
<ul>
<p><li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a></li>
<p><li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition</a></li>
</ul>
</section>
<section>
<h2 id="___sec6">Prerequisites </h2>
<p>
Basic knowledge in programming and mathematics, with an emphasis on
@@ -286,7 +296,7 @@ Python is the recurring programming language.
<section>
<h2 id="___sec6">Learning outcomes </h2>
<h2 id="___sec7">Learning outcomes </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -311,7 +321,7 @@ This course aims at giving you insights and knowledge about many of the central
<section>
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
<h2 id="___sec8">Topics covered in this course: Statistical analysis and optimization of data </h2>
<p>
The course has two central parts
@@ -344,7 +354,7 @@ The following topics will be covered
<section>
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
<h2 id="___sec9">Topics covered in this course: Machine Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -371,7 +381,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
<section>
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
<h2 id="___sec10">Extremely useful tools, strongly recommended </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -387,7 +397,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
<section>
<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
<h2 id="___sec11">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.
doc/pub/Intro2Course/html/Intro2Course-solarized.html
+22 -12
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@@ -239,7 +240,16 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec5">Prerequisites </h2>
<h2 id="___sec5">Recommended textbooks </h2>
<ul>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a></li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition</a></li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Prerequisites </h2>
<p>
Basic knowledge in programming and mathematics, with an emphasis on
@@ -255,7 +265,7 @@ Python is the recurring programming language.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Learning outcomes </h2>
<h2 id="___sec7">Learning outcomes </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -283,7 +293,7 @@ This course aims at giving you insights and knowledge about many of the central
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
<h2 id="___sec8">Topics covered in this course: Statistical analysis and optimization of data </h2>
<p>
The course has two central parts
@@ -318,7 +328,7 @@ The following topics will be covered
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
<h2 id="___sec9">Topics covered in this course: Machine Learning </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -345,7 +355,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
<h2 id="___sec10">Extremely useful tools, strongly recommended </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -362,7 +372,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
<h2 id="___sec11">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.
doc/pub/Intro2Course/html/Intro2Course.html
+22 -12
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@@ -71,25 +71,26 @@ div { text-align: justify; text-justify: inter-word; }
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@@ -244,7 +245,16 @@ Projects are handed in using <b>Canvas</b>. We use Github as repository for code
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec5">Prerequisites </h2>
<h2 id="___sec5">Recommended textbooks </h2>
<ul>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a></li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition</a></li>
</ul>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Prerequisites </h2>
<p>
Basic knowledge in programming and mathematics, with an emphasis on
@@ -260,7 +270,7 @@ Python is the recurring programming language.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec6">Learning outcomes </h2>
<h2 id="___sec7">Learning outcomes </h2>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -288,7 +298,7 @@ This course aims at giving you insights and knowledge about many of the central
<p>
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<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
<h2 id="___sec8">Topics covered in this course: Statistical analysis and optimization of data </h2>
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The course has two central parts
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<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
<h2 id="___sec9">Topics covered in this course: Machine Learning </h2>
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<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
<h2 id="___sec10">Extremely useful tools, strongly recommended </h2>
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<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
<h2 id="___sec11">Other courses on Data science and Machine Learning at UiO </h2>
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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.
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===== Recommended textbooks =====
* "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* "Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/"
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===== Prerequisites =====