From 7c3a4adcaa68692dde14345e795592f9c65a23d6 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 23 Aug 2021 08:48:47 +0200 Subject: [PATCH] update of first week --- doc/pub/week34/html/week34-bs.html | 80 ++++++++++--------- doc/pub/week34/html/week34-reveal.html | 51 +++++++++--- doc/pub/week34/html/week34-solarized.html | 53 +++++++++--- doc/pub/week34/html/week34.html | 53 +++++++++--- doc/pub/week34/ipynb/ipynb-week34-src.tar.gz | Bin 103349 -> 103349 bytes doc/pub/week34/ipynb/week34.ipynb | 46 ++++++++--- doc/src/week34/week34.do.txt | 39 ++++++--- 7 files changed, 223 insertions(+), 99 deletions(-) diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html index 6a5add6b4..7e1e9edca 100644 --- a/doc/pub/week34/html/week34-bs.html +++ b/doc/pub/week34/html/week34-bs.html @@ -42,6 +42,7 @@ Automatically generated HTML file from DocOnce source
  • Overview of first week
    • -
  • Thursday August 26
    • -
  • Lectures and ComputerLab
    • -
  • Course Format
    • -
  • Teachers
    • -
  • Deadlines for projects (tentative)
    • -
  • 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
    • -
  • Introduction
    • -
  • What is Machine Learning?
    • -
  • Types of Machine Learning
    • -
  • Software and needed installations
    • -
  • Python installers
    • -
  • Useful Python libraries
    • -
  • Installing R, C++, cython or Julia
    • -
  • Installing R, C++, cython, Numba etc
    • -
  • Numpy examples and Important Matrix and vector handling packages
    • -
  • Basic Matrix Features
    • -
  •    Some famous Matrices
    • -
  •    More Basic Matrix Features
    • -
  • Numpy and arrays
    • -
  • Matrices in Python
    • -
  • Meet the Pandas
    • -
  • Friday August 21
    • -
  • Reading Data and fitting
    • -
  • Friday August 21
    • -
  •    Simple linear regression model using scikit-learn
    • -
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • -
  •    Organizing our data
    • -
  •    Seeing the wood for the trees
    • -
  •    And what about using neural networks?
    • -
  • A first summary
    • +
  • Reading Recommendations
    • +
  • Thursday August 26
    • +
  • Lectures and ComputerLab
    • +
  • Course Format
    • +
  • Teachers
    • +
  • Deadlines for projects (tentative)
    • +
  • 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
    • +
  • Introduction
    • +
  • What is Machine Learning?
    • +
  • Types of Machine Learning
    • +
  • Software and needed installations
    • +
  • Python installers
    • +
  • Useful Python libraries
    • +
  • Installing R, C++, cython or Julia
    • +
  • Installing R, C++, cython, Numba etc
    • +
  • Numpy examples and Important Matrix and vector handling packages
    • +
  • Basic Matrix Features
    • +
  •    Some famous Matrices
    • +
  •    More Basic Matrix Features
    • +
  • Numpy and arrays
    • +
  • Matrices in Python
    • +
  • Meet the Pandas
    • +
  • Friday August 21
    • +
  • Reading Data and fitting
    • +
  • Friday August 27
    • +
  •    Simple linear regression model using scikit-learn
    • +
  •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    • +
  •    Organizing our data
    • +
  •    Seeing the wood for the trees
    • +
  •    And what about using neural networks?
    • +
  • A first summary
    • @@ -237,7 +239,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Aug 8, 2021

    +

    Aug 23, 2021


    @@ -261,7 +263,7 @@ MathJax.Hub.Config({

  • 9
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    • diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index 1c0c79740..44723bd74 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Aug 8, 2021

    +

    Aug 23, 2021


    @@ -166,6 +166,8 @@ MathJax.Hub.Config({

  • Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
    • @@ -307,10 +334,10 @@ Projects are handed in using Canvas. We use Github as repository for code

    -In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. +In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see below.

      -

    1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format.
      2. +

    2. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.

    3. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.

    @@ -835,14 +862,14 @@ $$

    - + - - - - - + + + + +
    Relations Name matrix elements
    Relations Name matrix elements
    \( A = A^{T} \) symmetric \( a_{ij} = a_{ji} \)
    \( A = \left (A^{T} \right )^{-1} \) real orthogonal \( \sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij} \)
    \( A = A^{ * } \) real matrix \( a_{ij} = a_{ij}^{ * } \)
    \( A = A^{\dagger} \) hermitian \( a_{ij} = a_{ji}^{ * } \)
    \( A = \left (A^{\dagger} \right )^{-1} \) unitary \( \sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij} \)
    \( A=A^{T} \) symmetric \( a_{ij}=a_{ji} \)
    \( A=\left (A^{T} \right )^{-1} \) real orthogonal \( \sum_k a_{ik}a_{jk}=\sum_k a_{ki} a_{kj} = \delta_{ij} \)
    \( A=A^{ * } \) real matrix \( a_{ij}=a_{ij}^{ * } \)
    \( A=A^{\dagger} \) hermitian \( a_{ij}=a_{ji}^{ * } \)
    \( A=\left (A^{\dagger} \right )^{-1} \) unitary \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \)
    @@ -1315,7 +1342,7 @@ But before we really start with nuclear physics data, let's just look at some si

    -

    Friday August 21

    +

    Friday August 27

    diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html index 0ea2111ac..2589481a4 100644 --- a/doc/pub/week34/html/week34-solarized.html +++ b/doc/pub/week34/html/week34-solarized.html @@ -62,6 +62,7 @@ div { text-align: justify; text-justify: inter-word; } +

    Aug 23, 2021












    @@ -203,6 +204,7 @@ MathJax.Hub.Config({