From 75835e62fd3756922383989d9dbb649cd1fc86ba Mon Sep 17 00:00:00 2001 From: mhjensen Date: Wed, 19 Aug 2020 13:08:42 +0200 Subject: [PATCH] small update on textbook --- .../html/._Intro2Course-bs000.html | 28 ++++---- .../html/._Intro2Course-bs001.html | 28 ++++---- .../html/._Intro2Course-bs002.html | 28 ++++---- .../html/._Intro2Course-bs003.html | 27 ++++---- .../html/._Intro2Course-bs004.html | 27 ++++---- .../html/._Intro2Course-bs005.html | 27 ++++---- .../html/._Intro2Course-bs006.html | 44 ++++++------- .../html/._Intro2Course-bs007.html | 60 ++++++++---------- .../html/._Intro2Course-bs008.html | 58 ++++++++--------- .../html/._Intro2Course-bs009.html | 59 ++++++++++------- .../html/._Intro2Course-bs010.html | 43 ++++++++----- .../html/._Intro2Course-bs011.html | 54 ++++++++-------- .../Intro2Course/html/Intro2Course-bs.html | 28 ++++---- .../html/Intro2Course-reveal.html | 22 +++++-- .../html/Intro2Course-solarized.html | 34 ++++++---- doc/pub/Intro2Course/html/Intro2Course.html | 34 ++++++---- .../ipynb/ipynb-Intro2Course-src.tar.gz | Bin 194 -> 195 bytes .../Intro2Course/pdf/Intro2Course-minted.pdf | Bin 235332 -> 236220 bytes doc/src/Intro2Course/Intro2Course.do.txt | 4 ++ 19 files changed, 332 insertions(+), 273 deletions(-) diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html index c788d6e82..746e88c22 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs000.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs000.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -155,7 +157,7 @@ end of tocinfo -->
  • 9
  • 10
  • ...
    • -
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs001.html b/doc/pub/Intro2Course/html/._Intro2Course-bs001.html index b2726f185..32b167a43 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs001.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs001.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -146,7 +148,7 @@ end of tocinfo -->
  • 10
  • 11
  • ...
    • -
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs002.html b/doc/pub/Intro2Course/html/._Intro2Course-bs002.html index fd8b0bbc7..e8470bffe 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs002.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs002.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -148,6 +150,8 @@ end of tocinfo -->
  • 10
  • 11
  • 12
    • +
  • ...
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs003.html b/doc/pub/Intro2Course/html/._Intro2Course-bs003.html index 9f216decf..cab235fd9 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs003.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs003.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -153,6 +155,7 @@ end of tocinfo -->
  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs004.html b/doc/pub/Intro2Course/html/._Intro2Course-bs004.html index afd7ac110..840640f88 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs004.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs004.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -163,6 +165,7 @@ end of tocinfo -->
  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html index d0cb88367..5abb14db1 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs005.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs005.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -149,6 +151,7 @@ Projects are handed in using Canvas. We use Github as repository for code
  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs006.html b/doc/pub/Intro2Course/html/._Intro2Course-bs006.html index 670b68d1a..adfb2eed2 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs006.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs006.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,20 +114,13 @@ end of tocinfo --> -

    Prerequisites

    +

    Recommended textbooks

    -

    -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. +

    -

    diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs007.html b/doc/pub/Intro2Course/html/._Intro2Course-bs007.html index 24443553c..4aabe5c46 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs007.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs007.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,31 +114,18 @@ end of tocinfo --> -

    Learning outcomes

    +

    Prerequisites

    -

    -
    -

    - -

    -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 - -

      -
    • Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
      • -
    • Be capable of extending the acquired knowledge to other systems and cases;
      • -
    • Have an understanding of central algorithms used in data analysis and machine learning;
      • -
    • Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;
      • -
    • Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
      • -
    • Learn about about decision trees, random forests, bagging and boosting methods;
      • -
    • Learn about support vector machines and kernel transformations;
      • -
    • Reduction of data sets, from PCA to clustering;
      • -
    • Autoencoders and Reinforcement Learning;
      • -
    • 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).
      • -
    -
    -
    - +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.

    @@ -155,6 +144,7 @@ This course aims at giving you insights and knowledge about many of the central

  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs008.html b/doc/pub/Intro2Course/html/._Intro2Course-bs008.html index 90095b8d2..a9c56f78a 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs008.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs008.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,17 +114,7 @@ end of tocinfo --> -

    Topics covered in this course: Statistical analysis and optimization of data

    - -

    -The course has two central parts - -

      -
    1. Statistical analysis and optimization of data
      2. -
    2. Machine learning
      3. -
    - -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 +

    Learning outcomes

    @@ -130,16 +122,19 @@ These topics will be scattered thorughout the course and may not necessarily be

    -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

    @@ -162,6 +157,7 @@ The following topics will be covered
  • 10
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  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs009.html b/doc/pub/Intro2Course/html/._Intro2Course-bs009.html index 8d95f7716..1d5d4241d 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs009.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs009.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,27 +114,35 @@ end of tocinfo --> -

    Topics covered in this course: Machine Learning

    +

    Topics covered in this course: Statistical analysis and optimization of data

    + +

    +The course has two central parts + +

      +
    1. Statistical analysis and optimization of data
      2. +
    2. Machine learning
      3. +
    + +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

    + +

    The following topics will be covered

      -
    • Linear Regression and Logistic Regression;
      • -
    • Neural networks and deep learning, including convolutional and recurrent neural networks
      • -
    • Decisions trees, Random Forests, Bagging and Boosting
      • -
    • Support vector machines
      • -
    • Bayesian linear and logistic regression
      • -
    • Boltzmann Machines
      • -
    • Unsupervised learning Dimensionality reduction, from PCA to cluster models
      • +
    • Basic concepts, expectation values, variance, covariance, correlation functions and errors;
      • +
    • Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
      • +
    • Central elements of Bayesian statistics and modeling;
      • +
    • Gradient methods for data optimization,
      • +
    • Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
      • +
    • Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
      • +
    • Principal Component Analysis (PCA) and its mathematical foundation
    - -Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics. - -

    @@ -154,6 +164,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs010.html b/doc/pub/Intro2Course/html/._Intro2Course-bs010.html index 475c87d10..ab8cdcb04 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs010.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs010.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,17 +114,27 @@ end of tocinfo --> -

    Extremely useful tools, strongly recommended

    +

    Topics covered in this course: Machine Learning

    +The following topics will be covered

      -
    • GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session
      • -
    • Anaconda and other Python environments, see intro slides and first exercise session
      • +
    • Linear Regression and Logistic Regression;
      • +
    • Neural networks and deep learning, including convolutional and recurrent neural networks
      • +
    • Decisions trees, Random Forests, Bagging and Boosting
      • +
    • Support vector machines
      • +
    • Bayesian linear and logistic regression
      • +
    • Boltzmann Machines
      • +
    • Unsupervised learning Dimensionality reduction, from PCA to cluster models
    + +Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics. + +

    @@ -144,6 +156,7 @@ end of tocinfo -->
  • 10
  • 11
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/._Intro2Course-bs011.html b/doc/pub/Intro2Course/html/._Intro2Course-bs011.html index b45de4df1..aa3a15a90 100644 --- a/doc/pub/Intro2Course/html/._Intro2Course-bs011.html +++ b/doc/pub/Intro2Course/html/._Intro2Course-bs011.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -112,26 +114,22 @@ end of tocinfo --> -

    Other courses on Data science and Machine Learning at UiO

    +

    Extremely useful tools, strongly recommended

    -The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. +

    +
    +

    -

      -
    1. STK2100 Machine learning and statistical methods for prediction and classification.
      2. -
    2. IN3050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
      3. -
    3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
      4. -
    4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
      5. -
    5. STK-IN4300 Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
      6. -
    6. INF4490 Biologically Inspired Computing. An introduction to self-adapting methods also called artificial intelligence or machine learning.
      7. -
    7. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
      8. -
    8. IN5400/INF5860 Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
      9. -
    9. TEK5040 Deep learning for autonomous systems. 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.
      10. -
    10. STK4051 Computational Statistics
      11. -
    11. STK4021 Applied Bayesian Analysis and Numerical Methods
      12. -
    +
      +
    • GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session
      • +
    • Anaconda and other Python environments, see intro slides and first exercise session
      • +
    +
    +
    +

    diff --git a/doc/pub/Intro2Course/html/Intro2Course-bs.html b/doc/pub/Intro2Course/html/Intro2Course-bs.html index c788d6e82..746e88c22 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-bs.html +++ b/doc/pub/Intro2Course/html/Intro2Course-bs.html @@ -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'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -91,12 +92,13 @@ end of tocinfo -->
  • Course Format
  • Teachers
  • Deadlines for projects (tentative)
    • -
  • Prerequisites
    • -
  • Learning outcomes
    • -
  • Topics covered in this course: Statistical analysis and optimization of data
    • -
  • Topics covered in this course: Machine Learning
    • -
  • Extremely useful tools, strongly recommended
    • -
  • Other courses on Data science and Machine Learning at UiO
    • +
  • Recommended textbooks
    • +
  • Prerequisites
    • +
  • Learning outcomes
    • +
  • Topics covered in this course: Statistical analysis and optimization of data
    • +
  • Topics covered in this course: Machine Learning
    • +
  • Extremely useful tools, strongly recommended
    • +
  • Other courses on Data science and Machine Learning at UiO
    • @@ -155,7 +157,7 @@ end of tocinfo -->
  • 9
  • 10
  • ...
    • -
  • 12
    • +
  • 13
  • »
    • diff --git a/doc/pub/Intro2Course/html/Intro2Course-reveal.html b/doc/pub/Intro2Course/html/Intro2Course-reveal.html index a9f60069c..7eb830837 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-reveal.html +++ b/doc/pub/Intro2Course/html/Intro2Course-reveal.html @@ -270,7 +270,17 @@ Projects are handed in using Canvas. We use Github as repository for code
    -

    Prerequisites

    +

    Recommended textbooks

    + + +
    + + +
    +

    Prerequisites

    Basic knowledge in programming and mathematics, with an emphasis on @@ -286,7 +296,7 @@ Python is the recurring programming language.

    -

    Learning outcomes

    +

    Learning outcomes

    @@ -311,7 +321,7 @@ This course aims at giving you insights and knowledge about many of the central
    -

    Topics covered in this course: Statistical analysis and optimization of data

    +

    Topics covered in this course: Statistical analysis and optimization of data

    The course has two central parts @@ -344,7 +354,7 @@ The following topics will be covered

    -

    Topics covered in this course: Machine Learning

    +

    Topics covered in this course: Machine Learning

    @@ -371,7 +381,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
    -

    Extremely useful tools, strongly recommended

    +

    Extremely useful tools, strongly recommended

    @@ -387,7 +397,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
    -

    Other courses on Data science and Machine Learning at UiO

    +

    Other courses on Data science and Machine Learning at UiO

    The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. diff --git a/doc/pub/Intro2Course/html/Intro2Course-solarized.html b/doc/pub/Intro2Course/html/Intro2Course-solarized.html index bfa55ec6f..9fd3cacc8 100644 --- a/doc/pub/Intro2Course/html/Intro2Course-solarized.html +++ b/doc/pub/Intro2Course/html/Intro2Course-solarized.html @@ -66,25 +66,26 @@ div { text-align: justify; text-justify: inter-word; } ('Course Format', 2, None, '___sec2'), ('Teachers', 2, None, '___sec3'), ('Deadlines for projects (tentative)', 2, None, '___sec4'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -239,7 +240,16 @@ Projects are handed in using Canvas. We use Github as repository for code











    -

    Prerequisites

    +

    Recommended textbooks

    + + + +









    + +

    Prerequisites

    Basic knowledge in programming and mathematics, with an emphasis on @@ -255,7 +265,7 @@ Python is the recurring programming language.











    -

    Learning outcomes

    +

    Learning outcomes

    @@ -283,7 +293,7 @@ This course aims at giving you insights and knowledge about many of the central











    -

    Topics covered in this course: Statistical analysis and optimization of data

    +

    Topics covered in this course: Statistical analysis and optimization of data

    The course has two central parts @@ -318,7 +328,7 @@ The following topics will be covered











    -

    Topics covered in this course: Machine Learning

    +

    Topics covered in this course: Machine Learning

    @@ -345,7 +355,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand











    -

    Extremely useful tools, strongly recommended

    +

    Extremely useful tools, strongly recommended

    @@ -362,7 +372,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand











    -

    Other courses on Data science and Machine Learning at UiO

    +

    Other courses on Data science and Machine Learning at UiO

    The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. diff --git a/doc/pub/Intro2Course/html/Intro2Course.html b/doc/pub/Intro2Course/html/Intro2Course.html index 6d15f0615..4744717ef 100644 --- a/doc/pub/Intro2Course/html/Intro2Course.html +++ b/doc/pub/Intro2Course/html/Intro2Course.html @@ -71,25 +71,26 @@ div { text-align: justify; text-justify: inter-word; } ('Course Format', 2, None, '___sec2'), ('Teachers', 2, None, '___sec3'), ('Deadlines for projects (tentative)', 2, None, '___sec4'), - ('Prerequisites', 2, None, '___sec5'), - ('Learning outcomes', 2, None, '___sec6'), + ('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', 2, None, - '___sec7'), + '___sec8'), ('Topics covered in this course: Machine Learning', 2, None, - '___sec8'), + '___sec9'), ('Extremely useful tools, strongly recommended', 2, None, - '___sec9'), + '___sec10'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec10')]} + '___sec11')]} end of tocinfo --> @@ -244,7 +245,16 @@ Projects are handed in using Canvas. We use Github as repository for code











    -

    Prerequisites

    +

    Recommended textbooks

    + + + +









    + +

    Prerequisites

    Basic knowledge in programming and mathematics, with an emphasis on @@ -260,7 +270,7 @@ Python is the recurring programming language.











    -

    Learning outcomes

    +

    Learning outcomes

    @@ -288,7 +298,7 @@ This course aims at giving you insights and knowledge about many of the central











    -

    Topics covered in this course: Statistical analysis and optimization of data

    +

    Topics covered in this course: Statistical analysis and optimization of data

    The course has two central parts @@ -323,7 +333,7 @@ The following topics will be covered











    -

    Topics covered in this course: Machine Learning

    +

    Topics covered in this course: Machine Learning

    @@ -350,7 +360,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand











    -

    Extremely useful tools, strongly recommended

    +

    Extremely useful tools, strongly recommended

    @@ -367,7 +377,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand











    -

    Other courses on Data science and Machine Learning at UiO

    +

    Other courses on Data science and Machine Learning at UiO

    The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. diff --git a/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz b/doc/pub/Intro2Course/ipynb/ipynb-Intro2Course-src.tar.gz index d97b0bbc27741f5693773b4d83d4487786d6582f..0f091ddb5ad24e761e0301db8cf184c7523bd672 100644 GIT binary patch literal 195 zcmV;!06hO6iwFRx2R&Z^1MSaC3c@fD2H>uHia9|^nzmVscF~0(;ssKg+W5%SBn5kW z`v6@jZi)!`Ha`PD!^}QkZMJ#j?k-piAtXx@7?Wq|lq8<#35^+0#wm{p%MuQRGM4fH z$Z{vWbk+$ooYI`6woNhrU34Lcc!89rHa=!*l7hXx zeSoeMH${YeS^NzAgqeM|+HAAX-Q8j~jIkt+VaPODj>)2F9`Q&37b)*w8uJ(!7b4*c zVDqi?(po1?ze;^lXcfNg^|h+{@XvG#Jo8T+YHcCkb*?4^B&Q0ZGvw$DMQdt}RiSP= wETAN1hXNRNYqe1zu>)C_+G#?02^vF3Bd<0LetnYXd7k%$2Q_Y#?EnY>05>63O#lD@ diff --git a/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf b/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf index 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We use Github as repository for codes, be !eblock +!split +===== 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/" !split ===== Prerequisites =====