diff --git a/Gaussian Processes.ipynb b/Gaussian Processes.ipynb
index c9075d07b26e97a125de26d166b562dc6c14bf4d..014f5c505ce9639e87ad474f9f13e5d7a3e55718 100644
--- a/Gaussian Processes.ipynb
+++ b/Gaussian Processes.ipynb
@@ -700,10 +700,21 @@
"http://www.gaussianprocess.org/gpml/chapters/RW.pdf"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "6274aac1",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "ec18ae45",
+ "id": "6d44d78a",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Mixture Models.ipynb b/Mixture Models.ipynb
index ead7c4fcfd28148517702d3b274577c5aa311ead..75ef86ed0e557a0f05fa14875389cd4dae2d2b36 100644
--- a/Mixture Models.ipynb
+++ b/Mixture Models.ipynb
@@ -656,6 +656,25 @@
" * Spectral clustering (see https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering). Uses K-means on another representation of the data. This representation of the data transforms the data points from the $xy$ space shown before into a space where connectedness of nearest neighbours is the criteria for similarity. For the mathematical foundation, this document may be useful: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.9323&rep=rep1&type=pdf\n",
" "
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "488df5eb",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "941c7cb8",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/README.md b/README.md
index 613a2c4839927a2ac23ee3352345d382b4318a35..d04ce0eed69eb826ec6fbe0d438fa1b94c5fa79d 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,21 @@
# ML Tutorial
-Some Jupyter notebooks to help beginners start using a selected set of ML methods.
\ No newline at end of file
+These are some hands-on Jupyter notebooks to help beginners start using a selected set of ML methods.
+When there was no time to delve into the details of the presentation, some extra details on the maths derivations
+are also given in the notebooks.
+
+Most of the notebooks require installing special software the following is a general setup that should work with most of the given notebooks.
+The list of packages needed in each given notebook is given in the beginning of the notebook with a `!pip install ...` command.
+
+```
+pip install torchvision torch pandas numpy matplotlib ipympl torchbnn
+```
+
+All the data used in the examples are produced on-the-fly for demonstration purposes, or are taken from public and open resources.
+None of the data comes from the EuXFEL, as the purpose of the examples is to show the idea behind the methods.
+
+### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!
+
+#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu
+#### Arman Davtyan: arman.davtyan@xfel.eu
+
diff --git a/Representation Learning.ipynb b/Representation Learning.ipynb
index babc8b6b51cebf89f53f80524e270d63140c9ef8..95fd59c6555004ed94fd0ffa0abeb3b0770c78b4 100644
--- a/Representation Learning.ipynb
+++ b/Representation Learning.ipynb
@@ -429,10 +429,21 @@
" * t-SNE embedding: If the objective is only to visualize the data, there are many alternative solutions which focus on reducing the dimensionality of the data into a 2D representation. The t-SNE method assumes that a Gaussian probability can be used to model similarity between data points in N dimensions and that one should maintain that similarity measure when projecting the data in two dimensions, assuming however that the 2D data points' similarity can be represented with a t-Student distribution. Full details on the method can be seen here: https://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf It can be easily tried in scikit-learn following the procedure here: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "18c90048",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "b189d3af",
+ "id": "e4f0d770",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Supervised classification.ipynb b/Supervised classification.ipynb
index 097354c041925d6cdf4e6aaa34f2e777a494ec2b..1a10549887ec13a2e8ca5a07094a787866a176ac 100644
--- a/Supervised classification.ipynb
+++ b/Supervised classification.ipynb
@@ -761,10 +761,21 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "bff1f8db",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "161b5464",
+ "id": "0879d703",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Supervised regression.ipynb b/Supervised regression.ipynb
index 17939882db5fc92ff7d4ea9a0de5c59aa330db51..f8e5a76466e1f1ea2233cb7595c16155987881e1 100644
--- a/Supervised regression.ipynb
+++ b/Supervised regression.ipynb
@@ -4378,10 +4378,21 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ccecaea7",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "8cfb5d35",
+ "id": "55635d1f",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Support Vector Machines.ipynb b/Support Vector Machines.ipynb
index 5451ea9c076a5ea62e1cf82e630dc72ea2b009d2..abce0ee6ab61908fe703a37d735a2541d413b37b 100644
--- a/Support Vector Machines.ipynb
+++ b/Support Vector Machines.ipynb
@@ -636,10 +636,21 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "df35963b",
+ "metadata": {},
+ "source": [
+ "### Contact us at the EuXFEL Data Analysis group at any time if you need help analysing your data!\n",
+ "\n",
+ "#### Danilo Ferreira de Lima: danilo.enoque.ferreira.de.lima@xfel.eu\n",
+ "#### Arman Davtyan: arman.davtyan@xfel.eu"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "84b20df8",
+ "id": "288d8fe2",
"metadata": {},
"outputs": [],
"source": []