-
@@ -261,7 +263,7 @@ MathJax.Hub.Config({
-
@@ -166,6 +166,8 @@ MathJax.Hub.Config({
+
+For the reading assignments we use the following abbreviations: + +
+ +Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html +
-Video of Lecture. +Video of Lecture from Fall Semester 2020. + +
+The lectures will be recorded and updated videos will be posted after the lectures.
-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.
@@ -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 | \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) |
@@ -203,6 +204,7 @@ MathJax.Hub.Config({
+
+
+
+
+For the reading assignments we use the following abbreviations: + +
-Video of Lecture. +Video of Lecture from Fall Semester 2020. + +
+The lectures will be recorded and updated videos will be posted after the lectures.
@@ -285,7 +312,7 @@ MathJax.Hub.Config({
| 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 | \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) |
-
diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html
index 5aae679dc..e90b09b95 100644
--- a/doc/pub/week34/html/week34.html
+++ b/doc/pub/week34/html/week34.html
@@ -67,6 +67,7 @@ div { text-align: justify; text-justify: inter-word; }
+
@@ -208,6 +209,7 @@ MathJax.Hub.Config({
+
+
+
+
+For the reading assignments we use the following abbreviations: + +
-Video of Lecture. +Video of Lecture from Fall Semester 2020. + +
+The lectures will be recorded and updated videos will be posted after the lectures.
@@ -290,7 +317,7 @@ MathJax.Hub.Config({
| 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 | \( \sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij} \) |
-
diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
index 6a6f35cf3..f0990de93 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 d11ea5dcd..9ff666182 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -10,7 +10,7 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Aug 8, 2021**\n",
+ "Date: **Aug 23, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -18,8 +18,13 @@
"\n",
"\n",
"\n",
+ "\n",
+ "\n",
+ "\n",
"## Overview of first week\n",
"\n",
+ " * Wednesday August 25: Introduction to software and repetition of Python Programming\n",
+ "\n",
" * Thursday August 26: First lecture: Presentation of the course, aims and content\n",
"\n",
" * Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics\n",
@@ -30,11 +35,28 @@
"\n",
"\n",
"\n",
+ "## Reading Recommendations\n",
+ "\n",
+ "For the reading assignments we use the following abbreviations:\n",
+ "* GBC: Goodfellow, Bengio, and Courville, Deep Learning\n",
+ "\n",
+ "* CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning\n",
+ "\n",
+ "* HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning\n",
+ "\n",
+ "* AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow\n",
+ "\n",
+ "Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at \n",
"\n",
- "
\n",
"\n",
@@ -1354,7 +1376,7 @@
"But before we really start with nuclear physics data, let's just look at some simpler polynomial fitting cases, such as,\n",
"(don't be offended) fitting straight lines!\n",
"\n",
- "## Friday August 21\n",
+ "## Friday August 27\n",
"\n",
"### Simple linear regression model using **scikit-learn**\n",
"\n",
diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index 51c16762b..66f0c69f1 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -4,22 +4,41 @@ DATE: today
+
+
+
!split
===== Overview of first week =====
!bblock
+ * Wednesday August 25: Introduction to software and repetition of Python Programming
* Thursday August 26: First lecture: Presentation of the course, aims and content
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics
* Friday August 27: Linear regression
* Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 25.
!eblock
+!split
+===== Reading Recommendations =====
+
+!bblock
+For the reading assignments we use the following abbreviations:
+* GBC: Goodfellow, Bengio, and Courville, Deep Learning
+* CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
+* HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
+* AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
+
+Reading recommendations this week: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
+!eblock
+
!split
===== Thursday August 26 =====
-"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage".
+"Video of Lecture from Fall Semester 2020":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/zoom_0.mp4?vrtx=view-as-webpage".
+
+The lectures will be recorded and updated videos will be posted after the lectures.
!split
===== Lectures and ComputerLab =====
@@ -61,7 +80,7 @@ _Teachers :_
* Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
* _Phone_: +47-48257387
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
- * _Office hours_: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.
+ * _Office hours_: *Anytime*! Individual or group office hours can be arranged either in person or via zoom. Feel free to send an email for planning.
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ452
@@ -94,9 +113,9 @@ Projects are handed in using _Canvas_. We use Github as repository for codes, be
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.
+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.
-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 Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.
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.
@@ -535,11 +554,11 @@ The inverse of a matrix is defined by
|----------------------------------------------------------------------|
| 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 | $\sum_k a_{ik}a_{jk}^{ * }=\sum_k a_{ki}^{ * } a_{kj}=\delta_{ij}$ |
|----------------------------------------------------------------------|
!eblock
@@ -906,7 +925,7 @@ But before we really start with nuclear physics data, let's just look at some si
(don't be offended) fitting straight lines!
!split
-===== Friday August 21 =====
+===== Friday August 27 =====
!split
=== Simple linear regression model using _scikit-learn_ ===
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 $\\sum_k a_{ik}a_{jk}^{ * }=\\sum_k a_{ki}^{ * } a_{kj}=\\delta_{ij}$