-
diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index 44723bd74..947448e2a 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -334,7 +334,7 @@ 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. 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. +In addition to the lecture 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.
-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 +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
@@ -462,7 +474,7 @@ Hands-on demonstrations, exercises and projects aim at deepening your understand
| 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 | \( a_{ij}=a_{ij}^{ < em>} \) |
| \( A=A^{\dagger} \) | hermitian | \( a_{ij}=a_{ji}^{ < /em>} \) |
| \( A=\left(A^{\dagger}\right )^{-1} \) | unitary | \( \sum_k a_{ik}a_{jk}^{ < em>}=\sum_k a_{ki}^{ < /em> } a_{kj}=\delta_{ij} \) |
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x)
@@ -1014,7 +1026,7 @@ To check the number of bytes (remember that one byte contains eight bits for dou
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x.itemsize)
Our first data set is going to be a classic from nuclear physics, namely all -available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves simply as an example here of a data set. +available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves merely as an example here of a data set.
We will show some of the @@ -1571,7 +1583,7 @@ plt.show()
The function coef gives us the parameter \( \beta \) of our fit while intercept yields -\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as +\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( \alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as
$$ MSE(\hat{y},\hat{\tilde{y}}) = \frac{1}{n}
\sum_{i=0}^{n-1}(y_i-\tilde{y}_i)^2,
@@ -1642,7 +1654,7 @@ ways of dealing with outliers.
The Huber cost function is defined as
$$
-H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}{a^{2}}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}\end{array}\right.
$$
diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html
index 2589481a4..292477361 100644
--- a/doc/pub/week34/html/week34-solarized.html
+++ b/doc/pub/week34/html/week34-solarized.html
@@ -191,7 +191,7 @@ MathJax.Hub.Config({
-
@@ -366,7 +366,7 @@ Projects are handed in using Canvas. We use Github as repository for code
-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 +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
| 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 | \( a_{ij}=a_{ij}^{ < em>} \) |
| \( A=A^{\dagger} \) | hermitian | \( a_{ij}=a_{ji}^{ < /em>} \) |
| \( A=\left(A^{\dagger}\right )^{-1} \) | unitary | \( \sum_k a_{ik}a_{jk}^{ < em>}=\sum_k a_{ki}^{ < /em> } a_{kj}=\delta_{ij} \) |
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x)
@@ -1016,7 +1028,7 @@ To check the number of bytes (remember that one byte contains eight bits for dou
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x.itemsize)
@@ -1321,7 +1333,7 @@ pretty close the basic mathematical expressions.
Our first data set is going to be a classic from nuclear physics, namely all -available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves simply as an example here of a data set. +available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves merely as an example here of a data set.
We will show some of the @@ -1556,7 +1568,7 @@ plt.show()
The function coef gives us the parameter \( \beta \) of our fit while intercept yields -\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as +\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( \alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as $$ MSE(\hat{y},\hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1}(y_i-\tilde{y}_i)^2, $$ @@ -1616,7 +1628,7 @@ ways of dealing with outliers.
The Huber cost function is defined as $$ -H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}. +H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}{a^{2}}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}\end{array}\right. $$ Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html index e90b09b95..59e5c7f86 100644 --- a/doc/pub/week34/html/week34.html +++ b/doc/pub/week34/html/week34.html @@ -196,7 +196,7 @@ MathJax.Hub.Config({
-
@@ -371,7 +371,7 @@ Projects are handed in using Canvas. We use Github as repository for code
-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 +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
| 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 | \( a_{ij}=a_{ij}^{ < em>} \) |
| \( A=A^{\dagger} \) | hermitian | \( a_{ij}=a_{ji}^{ < /em>} \) |
| \( A=\left(A^{\dagger}\right )^{-1} \) | unitary | \( \sum_k a_{ik}a_{jk}^{ < em>}=\sum_k a_{ki}^{ < /em> } a_{kj}=\delta_{ij} \) |
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x)
@@ -1021,7 +1033,7 @@ To check the number of bytes (remember that one byte contains eight bits for dou
import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0])
+x = np.log(np.array([4.0, 7.0, 8.0]))
print(x.itemsize)
@@ -1326,7 +1338,7 @@ pretty close the basic mathematical expressions.
Our first data set is going to be a classic from nuclear physics, namely all -available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves simply as an example here of a data set. +available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves merely as an example here of a data set.
We will show some of the @@ -1561,7 +1573,7 @@ plt.show()
The function coef gives us the parameter \( \beta \) of our fit while intercept yields -\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as +\( \alpha \). Depending on the constant in front of the normal distribution, we get values near or far from \( \alpha =2 \) and \( \beta =5 \). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as $$ MSE(\hat{y},\hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1}(y_i-\tilde{y}_i)^2, $$ @@ -1621,7 +1633,7 @@ ways of dealing with outliers.
The Huber cost function is defined as
$$
-H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}{a^{2}}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}\end{array}\right.
$$
Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016.png
index 10b85e2ca..b8976c975 100644
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diff --git a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png
index d72e85807..1c5a1b4b1 100644
Binary files a/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png and b/doc/pub/week34/ipynb/Results/FigureFiles/Masses2016Trees.png differ
diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
index f0990de93..744ad83f8 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 9ff666182..9cfc3d56e 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 23, 2021**\n",
+ "Date: **Aug 24, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -151,7 +151,7 @@
"\n",
"1. The lecture notes are collected as a jupyter-book at \n",
"\n",
- "
\n",
"\n",
@@ -655,19 +667,19 @@
"\n",
" * Diagonal if $a_{ij}=0$ for $i\\ne j$\n",
"\n",
- " * Upper triangular if $a_{ij}=0$ for $i > j$\n",
+ " * Upper triangular if $a_{ij}=0$ for $i>j$\n",
"\n",
- " * Lower triangular if $a_{ij}=0$ for $i < j$\n",
+ " * Lower triangular if $a_{ij}=0$ for $i 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 $\\sum_k a_{ik}a_{jk}^{*}=\\sum_k a_{ki}^{ * } a_{kj}=\\delta_{ij}$