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diff --git a/_images/08372136a6d1e6ee6c6b54d8c92314d2d944cd4f18761c8899b0c953527705ea.png b/_images/08372136a6d1e6ee6c6b54d8c92314d2d944cd4f18761c8899b0c953527705ea.png
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diff --git a/_sources/index.md b/_sources/index.md
@@ -25,8 +25,8 @@ ______________________________________________________________________
 ## Introduction
 
 This is a set of tutorials for the CMS Machine Learning Hands-on Advanced Tutorial Session (HATS). 
-They are intended to show you how to build machine learning models in python, using `Keras`, `TensorFlow`, and `PyTorch`, and use them in your `ROOT`-based analyses. 
-We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using dense and convolutional neural networks.
+They are intended to show you how to build machine learning models in python, using `xgboost`, `Keras`, `TensorFlow`, and `PyTorch`, and use them in your `ROOT`-based analyses. 
+ We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using BDTs, and dense and convolutional neural networks.
 We will also explore more advanced models such as graph neural networks (GNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) on simple datasets.
 
 ## Setup

diff --git a/_sources/notebooks/1-datasets-uproot.ipynb b/_sources/notebooks/1-datasets-uproot.ipynb
diff --git a/_sources/notebooks/2-boosted-decision-tree.ipynb b/_sources/notebooks/2-boosted-decision-tree.ipynb
diff --git a/_sources/notebooks/2-dense.md → _sources/notebooks/3-dense.md b/_sources/notebooks/2-dense.md → _sources/notebooks/3-dense.md
diff --git a/_sources/notebooks/2.1-dense-keras.ipynb → _sources/notebooks/3.1-dense-keras.ipynb b/_sources/notebooks/2.1-dense-keras.ipynb → _sources/notebooks/3.1-dense-keras.ipynb
diff --git a/_sources/notebooks/2.2-dense-pytorch.ipynb → _sources/notebooks/3.2-dense-pytorch.ipynb b/_sources/notebooks/2.2-dense-pytorch.ipynb → _sources/notebooks/3.2-dense-pytorch.ipynb
diff --git a/...oks/2.3-dense-bayesian-optimization.ipynb → ...oks/3.3-dense-bayesian-optimization.ipynb b/...oks/2.3-dense-bayesian-optimization.ipynb → ...oks/3.3-dense-bayesian-optimization.ipynb
diff --git a/_sources/notebooks/3-conv2d.ipynb → _sources/notebooks/4-conv2d.ipynb b/_sources/notebooks/3-conv2d.ipynb → _sources/notebooks/4-conv2d.ipynb
@@ -55,14 +55,13 @@
     "### Convolution Operation\n",
     "Two-dimensional convolutional layer for image height $H$, width $W$, number of input channels $C$, number of output kernels (filters) $N$, and kernel height $J$ and width $K$ is given by:\n",
     "\n",
-    "\\begin{align}\n",
-    "\\label{convLayer}\n",
-    "\\boldsymbol{Y}[v,u,n] &= \\boldsymbol{\\beta}[n] + \\sum_{c=1}^{C} \\sum_{j=1}^{J} \\sum_{k=1}^{K} \\boldsymbol{X}[v+j,u+k,c]\\, \\boldsymbol{W}[j,k,c,n]\\,,\n",
-    "\\end{align}\n",
+    "$$\n",
+    "\\boldsymbol{Y}[v,u,n] = \\boldsymbol{\\beta}[n] + \\sum_{c=1}^{C} \\sum_{j=1}^{J} \\sum_{k=1}^{K} \\boldsymbol{X}[v+j,u+k,c]\\, \\boldsymbol{W}[j,k,c,n]\\,,\n",
+    "$$\n",
     "\n",
     "where $Y$ is the output tensor of size $V \\times U \\times N$, $W$ is the weight tensor of size $J \\times K \\times C \\times N$ and $\\beta$ is the bias vector of length $N$ .\n",
     "\n",
-    "The example below has $C=1$ input channel and $N=1$ ($J\\times K=3\\times 3$) kernel [credit](https://towardsdatascience.com/types-of-convolution-kernels-simplified-f040cb307c37):\n",
+    "The example below has $C=1$ input channel and $N=1$ ($J\\times K=3\\times 3$) kernel ([credit](https://towardsdatascience.com/types-of-convolution-kernels-simplified-f040cb307c37)):\n",
     "\n",
     "![convolution](https://miro.medium.com/v2/resize:fit:780/1*Eai425FYQQSNOaahTXqtgg.gif)"
    ]
@@ -84,7 +83,7 @@
    "source": [
     "### Pooling\n",
     "\n",
-    "We also add pooling layers to reduce the image size between layers. For example, max pooling: (also from [here]([page](https://cs231n.github.io/convolutional-networks/))\n",
+    "We also add pooling layers to reduce the image size between layers. For example, max pooling (also from [here]([page](https://cs231n.github.io/convolutional-networks/))):\n",
     "\n",
     "![maxpool](https://cs231n.github.io/assets/cnn/maxpool.jpeg)"
    ]

diff --git a/_sources/notebooks/4-gnn-cora.ipynb → _sources/notebooks/5-gnn-cora.ipynb b/_sources/notebooks/4-gnn-cora.ipynb → _sources/notebooks/5-gnn-cora.ipynb
diff --git a/_sources/notebooks/5-vae-mnist.ipynb → _sources/notebooks/6-vae-mnist.ipynb b/_sources/notebooks/5-vae-mnist.ipynb → _sources/notebooks/6-vae-mnist.ipynb
diff --git a/_sources/notebooks/6-gan-mnist.ipynb → _sources/notebooks/7-gan-mnist.ipynb b/_sources/notebooks/6-gan-mnist.ipynb → _sources/notebooks/7-gan-mnist.ipynb
diff --git a/genindex.html b/genindex.html
@@ -175,17 +175,18 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="notebooks/1-datasets-uproot.html">1. Loading Datasets</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/2-dense.html">2. Dense networks</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.1-dense-keras.html">2.1. Dense neural network with Keras</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.2-dense-pytorch.html">2.2. Dense neural network with PyTorch</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.3-dense-bayesian-optimization.html">2.3. Optimize a dense network with Bayesian optimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/2-boosted-decision-tree.html">2. Boosted decision trees with xgboost</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/3-dense.html">3. Dense networks</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.1-dense-keras.html">3.1. Dense neural network with Keras</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.2-dense-pytorch.html">3.2. Dense neural network with PyTorch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.3-dense-bayesian-optimization.html">3.3. Optimize a dense network with Bayesian optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/3-conv2d.html">3. Convolutional Neural Networks for Jet-Images</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/4-gnn-cora.html">4. Graph Neural Network (GNN) with PyTorch Geometric</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/5-vae-mnist.html">5. Variational Autoencoders with Keras and MNIST</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/4-conv2d.html">4. Convolutional Neural Networks for Jet-Images</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/5-gnn-cora.html">5. Graph Neural Network (GNN) with PyTorch Geometric</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/6-vae-mnist.html">6. Variational Autoencoders with Keras and MNIST</a></li>
 
-<li class="toctree-l1"><a class="reference internal" href="notebooks/6-gan-mnist.html">7. Generative Adversarial Networks with Keras and MNIST</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/7-gan-mnist.html">8. Generative Adversarial Networks with Keras and MNIST</a></li>
 </ul>
 
     </div>

diff --git a/index.html b/index.html
@@ -177,17 +177,18 @@
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="notebooks/1-datasets-uproot.html">1. Loading Datasets</a></li>
-<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/2-dense.html">2. Dense networks</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.1-dense-keras.html">2.1. Dense neural network with Keras</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.2-dense-pytorch.html">2.2. Dense neural network with PyTorch</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/2.3-dense-bayesian-optimization.html">2.3. Optimize a dense network with Bayesian optimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/2-boosted-decision-tree.html">2. Boosted decision trees with xgboost</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/3-dense.html">3. Dense networks</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.1-dense-keras.html">3.1. Dense neural network with Keras</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.2-dense-pytorch.html">3.2. Dense neural network with PyTorch</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/3.3-dense-bayesian-optimization.html">3.3. Optimize a dense network with Bayesian optimization</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/3-conv2d.html">3. Convolutional Neural Networks for Jet-Images</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/4-gnn-cora.html">4. Graph Neural Network (GNN) with PyTorch Geometric</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/5-vae-mnist.html">5. Variational Autoencoders with Keras and MNIST</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/4-conv2d.html">4. Convolutional Neural Networks for Jet-Images</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/5-gnn-cora.html">5. Graph Neural Network (GNN) with PyTorch Geometric</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/6-vae-mnist.html">6. Variational Autoencoders with Keras and MNIST</a></li>
 
-<li class="toctree-l1"><a class="reference internal" href="notebooks/6-gan-mnist.html">7. Generative Adversarial Networks with Keras and MNIST</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/7-gan-mnist.html">8. Generative Adversarial Networks with Keras and MNIST</a></li>
 </ul>
 
     </div>
@@ -424,8 +425,8 @@ <h1>CMS Machine Learning Hands-on Advanced Tutorial Session (HATS)<a class="head
 <section id="introduction">
 <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h2>
 <p>This is a set of tutorials for the CMS Machine Learning Hands-on Advanced Tutorial Session (HATS).
-They are intended to show you how to build machine learning models in python, using <code class="docutils literal notranslate"><span class="pre">Keras</span></code>, <code class="docutils literal notranslate"><span class="pre">TensorFlow</span></code>, and <code class="docutils literal notranslate"><span class="pre">PyTorch</span></code>, and use them in your <code class="docutils literal notranslate"><span class="pre">ROOT</span></code>-based analyses.
-We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using dense and convolutional neural networks.
+They are intended to show you how to build machine learning models in python, using <code class="docutils literal notranslate"><span class="pre">xgboost</span></code>, <code class="docutils literal notranslate"><span class="pre">Keras</span></code>, <code class="docutils literal notranslate"><span class="pre">TensorFlow</span></code>, and <code class="docutils literal notranslate"><span class="pre">PyTorch</span></code>, and use them in your <code class="docutils literal notranslate"><span class="pre">ROOT</span></code>-based analyses.
+We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets using BDTs, and dense and convolutional neural networks.
 We will also explore more advanced models such as graph neural networks (GNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) on simple datasets.</p>
 </section>
 <section id="setup">