From 9535b698abf8be26be94f847c1e860cc21e5af98 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sun, 20 Aug 2023 22:10:08 +0200 Subject: [PATCH] book update --- .../_build/.doctrees/environment.pickle | Bin 167000 -> 167861 bytes .../_build/.doctrees/teachers.doctree | Bin 12510 -> 21633 bytes .../_build/.doctrees/textbooks.doctree | Bin 27057 -> 27172 bytes .../_build/html/_sources/teachers.md | 36 ++++++-- .../_build/html/_sources/textbooks.md | 3 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/_build/html/teachers.html | 86 +++++++++++++++--- doc/LectureNotes/_build/html/textbooks.html | 8 +- doc/LectureNotes/teachers.md | 36 ++++++-- doc/LectureNotes/textbooks.md | 3 +- 10 files changed, 140 insertions(+), 34 deletions(-) diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 685cf01e736f432e5b31e89cb312699d95484413..59afcda778e3f343a539009022b7ccaf0a356511 100644 GIT binary patch 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and 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\ No newline at end of file diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html index 154486556..cf7379ae1 100644 --- a/doc/LectureNotes/_build/html/teachers.html +++ b/doc/LectureNotes/_build/html/teachers.html @@ -316,14 +316,26 @@ const thebe_selector_output = ".output, .cell_output"
  • - - Teaching Assistants Fall semester 2022 + + Teaching Assistants Fall semester 2023 + +
    • +
  • + + Practicalities
  • Grading +
    • @@ -351,14 +363,26 @@ const thebe_selector_output = ".output, .cell_output"
  • - - Teaching Assistants Fall semester 2022 + + Teaching Assistants Fall semester 2023 + +
    • +
  • + + Practicalities
  • Grading +
    • @@ -381,18 +405,31 @@ const thebe_selector_output = ".output, .cell_output"

  • Office hours: Anytime! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.

    • -
    -

    Teaching Assistants Fall semester 2022

    +
    +

    Teaching Assistants Fall semester 2023

    +
    +

    Practicalities

    +
      +

    1. The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week.

      2. +

    2. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants.

      3. +

    3. On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. These lectures are recorded.

      4. +

    4. Three projects which are graded and count 1/3 each of the final grade;

      5. +

    5. A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade.

      6. +

    6. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;

      7. +

    7. The course is offered as a so-called cloned course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course;

      8. +

    8. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;

      9. +

    9. Weekly email with summary of activities will be mailed to all participants;

      10. +
    +

    Grading

    Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.

    @@ -405,6 +442,31 @@ const thebe_selector_output = ".output, .cell_output"

  • 40-45 points: E

  • 0-39 points: F-failed

    • +
    +

    In summary

    + + + + + + + + + + + + + + + + + + + + +

    Activity

    Percentage of total score

    First project, due October 9

    33.3% (1/3)

    Second project, due November 6

    33.3% (1.3)

    Third project, due December 11

    33.3% (1/3)

    Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)

    20%

    +

    The first weekly exercise set is scheduled for week 35.

    +
    diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html index d619ab034..0f8094a0f 100644 --- a/doc/LectureNotes/_build/html/textbooks.html +++ b/doc/LectureNotes/_build/html/textbooks.html @@ -366,9 +366,11 @@ The lecture notes are collected as a jupyter-book at

  • 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. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

  • 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 large extent this text.

    • -

  • Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html -The weekly plans will include reading suggestions from these two textbooks. -Additional textbooks:

    • +

  • Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html

    • + +

    The weekly plans will include reading suggestions from the above textbooks. +Additional textbooks:

    + diff --git a/doc/LectureNotes/teachers.md b/doc/LectureNotes/teachers.md index 9aece39a3..1f71151dc 100644 --- a/doc/LectureNotes/teachers.md +++ b/doc/LectureNotes/teachers.md @@ -8,14 +8,27 @@ * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470 * _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible. -## Teaching Assistants Fall semester 2022 -* Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no -* Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no +## Teaching Assistants Fall semester 2023 +* Karl Henrik Fredly, k.h.fredly@fys.uio.no +* Daniel Haas Becattini Lima, d.h.b.lima@fys.uio.no +* Adam Jakobsen, adam.jakobsen@fys.uio.no +* Fahimeh Najafi, fahimeh.najafi@fys.uio.no * Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no -* Joao Guilherme Carvalho Inacio, joaogca@fys.uio.no -* Sigurd Sørlie Rustad, s.s.rustad@fys.uio.no -* Stian Dysthe Bilek stian.bilek@fys.uio.no -* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no +* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no + + +## Practicalities + +1. The sessions on Tuesdays and Wednesdays last four hours for each group (four in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects. The sessions will begin with lectures and questions and answers about the material to be covered every week. +2. There are four groups, Tuesdays 815am-12pm and 1215pm-4pm and Wednesdays 815am-12pm and 1215pm-4pm. Please sign up as soon as possible for one of the groups. Max capacity per group is 30-40 participants. + +3. On Thursdays we have a regular lecture. These lectures start at 1215pm and end at 2pm. These lectures are recorded. +4. Three projects which are graded and count 1/3 each of the final grade; +5. A selected number of weekly assignments. The weekly assignments can be handed in and for all assignments you can get an extra score of 20 points to the final grade. +6. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo; +7. The course is offered as a so-called _cloned_ course, FYS-STK4155 at the Master of Science level and FYS-STK3155 as a senior undergraduate)course; +8. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html; +9. Weekly email with summary of activities will be mailed to all participants; ## Grading @@ -31,6 +44,13 @@ The final number of points is based on the average of all projects (including ev * 0-39 points: F-failed +### In summary +| Activity | Percentage of total score | +|------|-----| +| First project, _due October 9_ | 33.3% (1/3) | +| Second project, _due November 6_ | 33.3% (1.3) | +| Third project, _due December 11_ | 33.3% (1/3) | +| Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due each Friday)| 20% | - +The first weekly exercise set is scheduled for week 35. \ No newline at end of file diff --git a/doc/LectureNotes/textbooks.md b/doc/LectureNotes/textbooks.md index caff536dd..78bf22530 100644 --- a/doc/LectureNotes/textbooks.md +++ b/doc/LectureNotes/textbooks.md @@ -6,7 +6,8 @@ The lecture notes are collected as a jupyter-book at https://compphysics.github. - 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. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf - 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 large extent this text. - Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html -The weekly plans will include reading suggestions from these two textbooks. + +The weekly plans will include reading suggestions from the above textbooks. _Additional textbooks_: - 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. - 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.