diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index 09ff19fcf..1c0c79740 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -264,7 +264,16 @@ MathJax.Hub.Config({

  • Office: Department of Physics, University of Oslo, Eastern wing, room FØ452
    • -

    +

  • Stian Dysthe Bilek stian.bilek@fys.uio.no
    • + + +

  • Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
    • +

  • Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
    • +

  • Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
    • +

  • Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
    • @@ -277,9 +286,9 @@ MathJax.Hub.Config({
      -

    1. Project 1: September 28 (graded with feedback)
      2. -

    2. Project 2: November 2 (graded with feedback)
      3. -

    3. Project 3: December 7 (graded with feedback)
      4. +

    4. Project 1: September 27 (graded with feedback)
      5. +

    5. Project 2: November 1 (graded with feedback)
      6. +

    6. Project 3: December 6 (graded with feedback)

    @@ -293,10 +302,25 @@ Projects are handed in using Canvas. We use Github as repository for code

    - +
      +

    1. The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
      2. +
    +

    + +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. + +

      +

    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. 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.
      3. +
    +

    + +Additional textbooks: + +

      +

    1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
      2. +

    2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
      3. +
    diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html index 9761d1a77..0ea2111ac 100644 --- a/doc/pub/week34/html/week34-solarized.html +++ b/doc/pub/week34/html/week34-solarized.html @@ -294,6 +294,16 @@ MathJax.Hub.Config({
  • Office: Department of Physics, University of Oslo, Eastern wing, room FØ452
    • +
  • Stian Dysthe Bilek stian.bilek@fys.uio.no
    • + + + +
  • Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
    • +
  • Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
    • +
  • Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
    • +
  • Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
    • @@ -309,9 +319,9 @@ MathJax.Hub.Config({

      -
    1. Project 1: September 28 (graded with feedback)
      2. -
    2. Project 2: November 2 (graded with feedback)
      3. -
    3. Project 3: December 7 (graded with feedback)
      4. +
    4. Project 1: September 27 (graded with feedback)
      5. +
    5. Project 2: November 1 (graded with feedback)
      6. +
    6. Project 3: December 6 (graded with feedback)
    Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas. @@ -325,10 +335,23 @@ Projects are handed in using Canvas. We use Github as repository for code - +
      +
    1. The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
      2. +
    + +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. + +
      +
    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. 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.
      3. +
    + +Additional textbooks: + +
      +
    1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
      2. +
    2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
      3. +










    diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html index 655a60299..5aae679dc 100644 --- a/doc/pub/week34/html/week34.html +++ b/doc/pub/week34/html/week34.html @@ -299,6 +299,16 @@ MathJax.Hub.Config({
  • Office: Department of Physics, University of Oslo, Eastern wing, room FØ452
    • +
  • Stian Dysthe Bilek stian.bilek@fys.uio.no
    • + + + +
  • Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
    • +
  • Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
    • +
  • Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
    • +
  • Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
    • @@ -314,9 +324,9 @@ MathJax.Hub.Config({

      -
    1. Project 1: September 28 (graded with feedback)
      2. -
    2. Project 2: November 2 (graded with feedback)
      3. -
    3. Project 3: December 7 (graded with feedback)
      4. +
    4. Project 1: September 27 (graded with feedback)
      5. +
    5. Project 2: November 1 (graded with feedback)
      6. +
    6. Project 3: December 6 (graded with feedback)
    Projects are handed in using Canvas. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via Canvas. @@ -330,10 +340,23 @@ Projects are handed in using Canvas. We use Github as repository for code - +
      +
    1. The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
      2. +
    + +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. + +
      +
    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. 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.
      3. +
    + +Additional textbooks: + +
      +
    1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
      2. +
    2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
      3. +










    diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz index 99aa99feb..6a6f35cf3 100644 Binary files a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz and b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz differ diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index 5b6231e4d..d11ea5dcd 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -95,15 +95,29 @@ " * **Office**: Department of Physics, University of Oslo, Eastern wing, room FØ452\n", "\n", "\n", + "* Stian Dysthe Bilek stian.bilek@fys.uio.no\n", + "\n", + " * **Office**: Department of Physics, University of Oslo, Eastern wing, room FØ450\n", + "\n", + "\n", + "* Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no\n", + "\n", + "* Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no\n", + "\n", + "* Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no\n", + "\n", + "* Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no\n", + "\n", + "\n", "\n", "## Deadlines for projects (tentative)\n", "\n", "\n", - "1. Project 1: September 28 (graded with feedback)\n", + "1. Project 1: September 27 (graded with feedback)\n", "\n", - "2. Project 2: November 2 (graded with feedback)\n", + "2. Project 2: November 1 (graded with feedback)\n", "\n", - "3. Project 3: December 7 (graded with feedback)\n", + "3. Project 3: December 6 (graded with feedback)\n", "\n", "Projects are handed in using **Canvas**. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via **Canvas**.\n", "\n", @@ -112,9 +126,20 @@ "\n", "## Recommended textbooks\n", "\n", - "* [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570)\n", "\n", - "* [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/)\n", + "1. The lecture notes are collected as a jupyter-book at \n", + "\n", + "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.\n", + "\n", + "1. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 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.\n", + "\n", + "2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at 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.\n", + "\n", + "Additional textbooks:\n", + "\n", + "1. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, This is a well-known text and serves as additional literature.\n", + "\n", + "2. Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.\n", "\n", "## Prerequisites\n", "\n", diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt index b6d8fa959..51c16762b 100644 --- a/doc/src/week34/week34.do.txt +++ b/doc/src/week34/week34.do.txt @@ -65,7 +65,12 @@ _Teachers :_ * Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ452 - +* Stian Dysthe Bilek stian.bilek@fys.uio.no + * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ450 +* Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no +* Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no +* Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no +* Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no !eblock @@ -74,9 +79,9 @@ _Teachers :_ !bblock -o Project 1: September 28 (graded with feedback) -o Project 2: November 2 (graded with feedback) -o Project 3: December 7 (graded with feedback) +o Project 1: September 27 (graded with feedback) +o Project 2: November 1 (graded with feedback) +o Project 3: December 6 (graded with feedback) Projects are handed in using _Canvas_. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via _Canvas_. @@ -86,8 +91,22 @@ Projects are handed in using _Canvas_. We use Github as repository for codes, be !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/" + +o The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. + +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. + +o 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. + +o 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. + +Additional textbooks: + +o Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature. + +o Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course. + + !split ===== Prerequisites =====