diff --git a/exercises/release/exercise-10/1__Colorblind_simulator.ipynb b/exercises/release/exercise-10/1__Colorblind_simulator.ipynb
new file mode 100644
index 0000000..fe828d9
--- /dev/null
+++ b/exercises/release/exercise-10/1__Colorblind_simulator.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import imageio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# slicing, broadcasting, views and dtypes in numpy\n",
+    "\n",
+    "We are going to review slicing with numpy arrays and how images are stored. Remember, a grayscale image is a 2D array like:\n",
+    "\n",
+    "```\n",
+    "np.array([[1, 2, 3, 4],\n",
+    "          [5, 6, 7, 8]])\n",
+    "```\n",
+    "\n",
+    "The size of this image would be 2 pixels high and 4 pixels wide. The numpy shape of this image is `(2, 4)`.\n",
+    "\n",
+    "Color images are stored as 3 \"channels\", with such a 2D array. These channels are red, green and blue, and correspond to the wavelength sensitivity of cameras and to the display wavelengths of computer monitors and projectors. The channel is stored as the third and final dimension and thus the shape is `(height, width, n_channels)`. For most color images such as JPEGs, these are stored as RGB images, `n_channels` is 3, and the channels are ordered red, green and blue.\n",
+    "\n",
+    "Let's consider this array:\n",
+    "\n",
+    "```\n",
+    "im = np.zeros((100, 200, 3))\n",
+    "```\n",
+    "\n",
+    "This array is 200 pixels width 100 pixels high (shape `(100, 200, 3)`), all zeros.\n",
+    "\n",
+    "## slicing\n",
+    "\n",
+    "Now let's use *slicing* to set all pixels in the red channel to have a single value of 255.\n",
+    "\n",
+    "```\n",
+    "im[:,:,0] = 255 # set the red channel to have value 255\n",
+    "```\n",
+    "\n",
+    "## broadcasting\n",
+    "\n",
+    "This is called *broadcasting* because the single value was \"broadcast\" - expanded - to the shape of the array.\n",
+    "\n",
+    "We can also set all pixels in a channel to from another array. For example, let's create a new array of shape `(100, 200)` and then use this to set a particular color channel:\n",
+    "\n",
+    "```\n",
+    "new_channel = np.ones((100,200))\n",
+    "im[:,:,1] = new_channel\n",
+    "```\n",
+    "\n",
+    "This will set the green channel to have value 1.\n",
+    "\n",
+    "## views\n",
+    "\n",
+    "In addition to using slicing to set values in an array, we can also use slicing to get a *view* of the array:\n",
+    "\n",
+    "```\n",
+    "blue_channel = im[:,:,2]\n",
+    "```\n",
+    "\n",
+    "Now, `blue_channel` has shape `(100, 200)` and is a view of the memory in `im`. It acts like a 2D array (instead of a 3D array) but does not have its own data and instead references the data in the `im` array.\n",
+    "\n",
+    "## dtype\n",
+    "\n",
+    "The last step of our mini-review is the concept of data types, or dtypes. Remember that numpy arrays are collections of identical types. For images, we most often use the `uint8` type, or unsigned 8 bit integers, which can have a value from 0 to 255. (From zero to 2^8-1.) Sometimes, we want to do math with higher precision, and thus we can convert the data to a floating point type, such as `float64`, a 64 bit floating point number:\n",
+    "\n",
+    "```\n",
+    "im_float = im.astype(np.float64)\n",
+    "```\n",
+    "\n",
+    "So, for example you could average together two `uint8` arrays of the same size by doing this:\n",
+    "\n",
+    "```\n",
+    "result_float = (im_a.astype(np.float64) + im_b.astype(np.float64)) / 2.0\n",
+    "```\n",
+    "\n",
+    "You may want to convert this average array back to `uint8`, which would be done like so:\n",
+    "\n",
+    "```\n",
+    "result = result_float.astype(np.uint8)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Colorblind simulator\n",
+    "\n",
+    "We are now going to use numpy operations on an image to create a very basic \"colorblind simulator\". First, let's load an image. We can use any image, but here we load a image from Ishihara's test for color deficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_rgb = imageio.imread('Ishihara_Plate_3.jpg')\n",
+    "fig, ax = plt.subplots(ncols=1,nrows=1, figsize=(4,3))\n",
+    "ax.imshow(im_rgb);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1. The task\n",
+    "\n",
+    "Now, make a simple simulatation of deuteranopia (red-green color blindness) by averaging together the red and green channels.\n",
+    "\n",
+    "1. Create a view of the red channel, convert it to float\n",
+    "2. Create a view of the green channel, convert it to float\n",
+    "3. Add the elements of float dtype arrays together and divide by two to create an average of the two channels.\n",
+    "4. Convert your average back to uint8\n",
+    "5. Create a new image which is a copy of your original image.\n",
+    "5. Set the red and green channels in your new image to the average red-green image.\n",
+    "6. Save the result in an array called `colorblind_simulated_image`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "93885197b1b5ef72377adddea21df1a4",
+     "grade": false,
+     "grade_id": "cell-3f469848e72f5234",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "246348f235d54484bc41c841a715e97c",
+     "grade": true,
+     "grade_id": "cell-1529edd9cffd843e",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# If this runs without error, it means the answer in your previous cell was correct.\n",
+    "assert(np.allclose(colorblind_simulated_image[:,:,0], colorblind_simulated_image[:,:,1]))\n",
+    "for i in np.arange(0,im_rgb.shape[0],50):\n",
+    "    for j in np.arange(0,im_rgb.shape[1],50):\n",
+    "        assert(colorblind_simulated_image[i,j,0]==int((im_rgb[i,j,0].astype(np.float64)+im_rgb[i,j,1].astype(np.float64))*0.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2. Display your image\n",
+    "\n",
+    "Now use `imshow()` from matplotlib to view your image.\n",
+    "\n",
+    "Your result should look like the right panel here:\n",
+    "\n",
+    "![side-by-side.png](side-by-side.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "b9fce3b88ec8f29eb36014964a200e55",
+     "grade": true,
+     "grade_id": "cell-aafb273354efd895",
+     "locked": false,
+     "points": 1,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/exercises/release/exercise-10/2__Image_color_clustering.ipynb b/exercises/release/exercise-10/2__Image_color_clustering.ipynb
new file mode 100644
index 0000000..65a11eb
--- /dev/null
+++ b/exercises/release/exercise-10/2__Image_color_clustering.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Clustering to do *color quantization* using K-Means\n",
+    "\n",
+    "\n",
+    "First, we review the clustering example from lecture. We will perform a pixel-wise Vector Quantization (VQ) of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615\n",
+    "unique colors to a smaller number, while preserving the overall appearance quality.\n",
+    "\n",
+    "In this example, pixels are represented in a 3D-space and K-means is used to find k color clusters. In the image processing literature, the **codebook** obtained from K-means (the cluster centers) is called the color palette. Using an unsigned 8 bit integer (called a byte), up to 256 colors can be addressed (numbered 0-255), whereas an RGB encoding requires 3 bytes per pixel. The GIF file format, for example, uses such a palette.\n",
+    "\n",
+    "For comparison, a quantized image using a random codebook (colors picked up randomly) is also shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Authors: Robert Layton <robertlayton@gmail.com>\n",
+    "#          Olivier Grisel <olivier.grisel@ensta.org>\n",
+    "#          Mathieu Blondel <mathieu@mblondel.org>\n",
+    "#\n",
+    "# License: BSD 3 clause\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import pairwise_distances_argmin\n",
+    "from sklearn.datasets import load_sample_image\n",
+    "from sklearn.utils import shuffle\n",
+    "from scipy import ndimage\n",
+    "import imageio.v2 as imageio\n",
+    "from time import time\n",
+    "\n",
+    "# Load the Summer Palace photo\n",
+    "china = load_sample_image(\"china.jpg\")\n",
+    "\n",
+    "# Convert to floats instead of the default 8 bits integer coding. Dividing by\n",
+    "# 255 is important so that plt.imshow behaves works well on float data (need to\n",
+    "# be in the range [0-1])\n",
+    "china = np.array(china, dtype=np.float64) / 255\n",
+    "\n",
+    "# Load image and transform to a 2D numpy array which is w*h rows and d columns.\n",
+    "h, w, d = original_shape = tuple(china.shape)\n",
+    "assert d == 3\n",
+    "image_array = np.reshape(china, (w * h, d))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's plot the color intensity values for a sample of 1000 pixels\n",
+    "\n",
+    "# First, let's sample 1000 pixels randomly\n",
+    "image_array_sample = shuffle(image_array, random_state=0)[:1000]\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12,3))\n",
+    "\n",
+    "axes[0].plot(image_array_sample[:,0], image_array_sample[:,1], '.')\n",
+    "axes[0].set_xlabel('red')\n",
+    "axes[0].set_ylabel('green')\n",
+    "\n",
+    "axes[1].plot(image_array_sample[:,0], image_array_sample[:,2], '.')\n",
+    "axes[1].set_xlabel('red')\n",
+    "axes[1].set_ylabel('blue')\n",
+    "\n",
+    "axes[2].plot(image_array_sample[:,1], image_array_sample[:,2], '.')\n",
+    "axes[2].set_xlabel('green')\n",
+    "axes[2].set_ylabel('blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We will use this function later to recreate a full color image given a particular codebook.\n",
+    "\n",
+    "def recreate_image(codebook, labels, h, w):\n",
+    "    \"\"\"Recreate the (compressed) image from the code book & labels\"\"\"\n",
+    "    d = codebook.shape[1]\n",
+    "    image = np.zeros((h, w, d))\n",
+    "    label_idx = 0\n",
+    "    for i in range(h):\n",
+    "        for j in range(w):\n",
+    "            image[i][j] = codebook[labels[label_idx]]\n",
+    "            label_idx += 1\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_colors = 10\n",
+    "\n",
+    "# Here with use the KMeans algorithm to cluster a subset of our\n",
+    "print(\"Fitting model on a small sub-sample of the data\")\n",
+    "t0 = time()\n",
+    "image_array_sample = shuffle(image_array, random_state=0)[:1000]\n",
+    "kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)\n",
+    "print(\"done in %0.3fs.\" % (time() - t0))\n",
+    "\n",
+    "# Get labels for all points\n",
+    "print(\"Predicting color indices on the full image (k-means)\")\n",
+    "t0 = time()\n",
+    "labels = kmeans.predict(image_array)\n",
+    "print(\"done in %0.3fs.\" % (time() - t0))\n",
+    "print('np.max(labels)',np.max(labels))\n",
+    "\n",
+    "codebook_random = shuffle(image_array, random_state=0)[:n_colors]\n",
+    "print(\"Predicting color indices on the full image (random)\")\n",
+    "t0 = time()\n",
+    "labels_random = pairwise_distances_argmin(codebook_random,\n",
+    "                                          image_array,\n",
+    "                                          axis=0)\n",
+    "print(\"done in %0.3fs.\" % (time() - t0))\n",
+    "print('np.max(labels_random)',np.max(labels_random))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Display all results, alongside original image\n",
+    "figsize = (10,10)\n",
+    "plt.figure(1,figsize=figsize)\n",
+    "plt.clf()\n",
+    "ax = plt.axes([0, 0, 1, 1])\n",
+    "plt.axis('off')\n",
+    "plt.title('Original image (96,615 colors)')\n",
+    "plt.imshow(china)\n",
+    "\n",
+    "plt.figure(2,figsize=figsize)\n",
+    "plt.clf()\n",
+    "ax = plt.axes([0, 0, 1, 1])\n",
+    "plt.axis('off')\n",
+    "plt.title('Quantized image (%d colors, K-Means)' % n_colors)\n",
+    "plt.imshow(recreate_image(kmeans.cluster_centers_, labels, h, w))\n",
+    "\n",
+    "plt.figure(3,figsize=figsize)\n",
+    "plt.clf()\n",
+    "ax = plt.axes([0, 0, 1, 1])\n",
+    "plt.axis('off')\n",
+    "plt.title('Quantized image (%d colors, Random)' % n_colors)\n",
+    "plt.imshow(recreate_image(codebook_random, labels_random, h, w))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Color quantization for coffee bean detection and localization\n",
+    "\n",
+    "We now want to use color quantization to segment an image into specific objects based on their color.\n",
+    "\n",
+    "We now load the image `beans` as so:\n",
+    "\n",
+    "```\n",
+    "beans = imageio.imread('data/IMG_4272.JPG')\n",
+    "```\n",
+    "\n",
+    "This is a copy of the code above modified to use the `beans` data (instead of the `china` data). Below we show the original image and a color quantized image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beans = imageio.imread('coffee_beans.JPG')\n",
+    "\n",
+    "beans = np.array(beans, dtype=np.float64) / 255\n",
+    "\n",
+    "# Load image and transform to a 2D numpy array which is w*h rows and d columns.\n",
+    "h, w, d = beans_shape = tuple(beans.shape)\n",
+    "assert d == 3\n",
+    "image_array = np.reshape(beans, (w * h, d))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1 Choose `n_colors` to perform well in subsequent tasks.\n",
+    "\n",
+    "We have a goal of choosing `n_colors` (the number of clusters, *k*) which will make the task of detecting beans easiest. So change this variable and re-run the following cells until you have a good value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "d223e9eb113554dfd10cada250c66730",
+     "grade": true,
+     "grade_id": "cell-4e76633799a3843b",
+     "locked": false,
+     "points": 1,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here with use the KMeans algorithm to cluster a subset of our\n",
+    "print(\"Fitting model on a small sub-sample of the data\")\n",
+    "t0 = time()\n",
+    "image_array_sample = shuffle(image_array, random_state=0)[:1000]\n",
+    "kmeans = KMeans(n_clusters=n_colors, random_state=0, n_init='auto').fit(image_array_sample)\n",
+    "print(\"done in %0.3fs.\" % (time() - t0))\n",
+    "\n",
+    "# Get labels for all points\n",
+    "print(\"Predicting color indices on the full image (k-means)\")\n",
+    "t0 = time()\n",
+    "labels = kmeans.predict(image_array)\n",
+    "print(\"done in %0.3fs.\" % (time() - t0))\n",
+    "print('np.max(labels)',np.max(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For this task (bean detection) it will be useful to also plot the labels, so \n",
+    "# we shape them to have the same size as our images.\n",
+    "\n",
+    "labels_shaped = labels.copy()\n",
+    "labels_shaped.shape = (h,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the original, reconstructed and label images\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=3, figsize=(10,20))\n",
+    "axes[0].set_title('Original image')\n",
+    "axes[0].imshow(beans)\n",
+    "\n",
+    "axes[1].set_title('Quantized image (%d colors, K-Means)' % n_colors)\n",
+    "axes[1].imshow(recreate_image(kmeans.cluster_centers_, labels, h, w))\n",
+    "\n",
+    "axes[1].set_title('Labels (%d colors, K-Means)' % n_colors)\n",
+    "cax=axes[2].imshow(labels_shaped, cmap='jet')\n",
+    "fig.colorbar(cax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Image processing for bean detection\n",
+    "\n",
+    "Now that we have quantized our colors, perhaps we can use a specific quantized color to simply an image analysis task.\n",
+    "\n",
+    "In this case, our \"labels\" indicate which cluster each pixel belongs to."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2 Based on the label plot above, pick the label number with the best value for our couting task.\n",
+    "\n",
+    "Put this in the `best_label` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "55b9d4257936aa3d40774b4296978b07",
+     "grade": true,
+     "grade_id": "cell-a1545b44baba6de9",
+     "locked": false,
+     "points": 1,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3 The following steps do blurring, thresholding, erosion, and connected components labeling to \"clean up\" the labels. Run and adjust these steps, if necessary, to make an image showing the beans with their centers roughly like this:\n",
+    "\n",
+    "(It is not required, or expected, that your image will look exactly like this.)\n",
+    "\n",
+    "See if you can get even better bean localizations.\n",
+    "\n",
+    "![bean-centers.png](bean-centers.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresholded = labels_shaped==best_label\n",
+    "plt.imshow(thresholded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = ndimage.gaussian_filter(thresholded.astype(np.float64), sigma=8.0)\n",
+    "plt.imshow(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresholded = im > 0.5\n",
+    "plt.imshow(thresholded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eroded = thresholded\n",
+    "for i in range(10):\n",
+    "    eroded = ndimage.binary_erosion(eroded)\n",
+    "plt.imshow(eroded.astype(np.uint8))\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))\n",
+    "labels, num_labels = ndimage.label(thresholded)\n",
+    "plt.imshow(labels, cmap='jet')\n",
+    "plt.colorbar();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coms = ndimage.center_of_mass(thresholded, labels, index=range(1,num_labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))\n",
+    "plt.imshow(beans)\n",
+    "plt.colorbar()\n",
+    "for com in coms:\n",
+    "    ax.plot([com[1]],[com[0]], 'x')\n",
+    "plt.savefig('bean-centers.png')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/exercises/release/exercise-10/3__Clustering_colorimetric_RT-LAMP_images.ipynb b/exercises/release/exercise-10/3__Clustering_colorimetric_RT-LAMP_images.ipynb
new file mode 100644
index 0000000..dcd7971
--- /dev/null
+++ b/exercises/release/exercise-10/3__Clustering_colorimetric_RT-LAMP_images.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The year is 2021 and you work at a SARS-CoV-2 PCR test center and your expensive qPCR machine has broken. But the school kids in your town urgently need their test results in order to participate safely in the big Nicolaus performance they have been preparing for all year. Luckily, your team also has a colorimetric dye - it changes color depending on DNA concentration - and all the reagents required to perform an alternative reaction to PCR called LAMP. In terms of equipment, all you need is a heating block. Luckily, you have one which is functional. A LAMP reaction requires only that the temperature required for the reaction is held constant. (Typically this is about 65 degrees C.) You reason that if you can implement a reliable readout for colorimetric LAMP, you will be able to run the tests using LAMP and allow the kids to safely participate in their performance.\n",
+    "\n",
+    "LAMP works similarly to PCR in that, if a particular sample is \"positive\", the reaction was able to amplify DNA because the LAMP primers matched a complementary sequence in the sample, and thus the LAMP reaction could generate large amounts of amplified product DNA. When using a colorimetric dye, the color changes from the \"negative color\" indicating low DNA concentration to the \"positive color\" indicating high DNA concentration. Here we use a dye from the NEB Colorimetric WarmStart LAMP Kit which is yellow when positive and red when negative. (Other colorimetric dyes such as Hydroxy Naphthol Blue - HNB - also are available and may have different colors.)\n",
+    "\n",
+    "To implement the reliable readout, you decide to implement a computer vision program that eliminates the need for subjective color judgement and potential errors by colorblind technicians. Based on what you have learned in a bioinformatics class, you have decided to perform \"color quantization\" on images acquired with a mobile phone camera to then turn full-color images into a single binarized image showing the locations of the wells with positive samples.\n",
+    "\n",
+    "In this exercise, we use an image from [this preprint](https://www.medrxiv.org/content/10.1101/2020.05.05.20092288v1) which was later published as [this paper](https://stm.sciencemag.org/content/12/556/eabc7075/tab-article-info)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns; sns.set()  # for plot styling\n",
+    "import numpy as np\n",
+    "import scipy.misc\n",
+    "from scipy import ndimage\n",
+    "import imageio.v2 as imageio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_rgb = imageio.imread('RT-LAMP-Dao-Thi-2020-plate.png')\n",
+    "# Image CC-BY-ND 4.0 International license, copyright the authors of Dao Thi et al. (2020)\n",
+    "ax = plt.axes(xticks=[], yticks=[])\n",
+    "ax.imshow(im_rgb);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_rgb.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = im_rgb / 255.0 # use 0...1 scale\n",
+    "data = data.reshape(318 * 488, 3)\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_pixels(data, title, colors=None, N=10000):\n",
+    "    if colors is None:\n",
+    "        colors = data\n",
+    "    \n",
+    "    # choose a random subset\n",
+    "    rng = np.random.RandomState(0)\n",
+    "    i = rng.permutation(data.shape[0])[:N]\n",
+    "    colors = colors[i]\n",
+    "    R, G, B = data[i].T\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "    ax[0].scatter(R, G, color=colors, marker='.')\n",
+    "    ax[0].set(xlabel='Red', ylabel='Green', xlim=(0, 1), ylim=(0, 1))\n",
+    "\n",
+    "    ax[1].scatter(R, B, color=colors, marker='.')\n",
+    "    ax[1].set(xlabel='Red', ylabel='Blue', xlim=(0, 1), ylim=(0, 1))\n",
+    "\n",
+    "    fig.suptitle(title, size=20)\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_pixels(data, title='Input color space: 16 million possible colors');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Q1 Determine a value of *k*, the number of clusters, that enables a \"good\" clustering of the colors which will be useful in creating a reliable program.\n",
+    "\n",
+    "You will probably want to test several values of `k` and use the one that works best for your task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "fcc8254fee316bd424fd51a4be0bd46a",
+     "grade": false,
+     "grade_id": "cell-fd2ecd26bf20e3a8",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "e0803d1d18053dd0638f9fe007f0dc47",
+     "grade": true,
+     "grade_id": "cell-c5493191b6b698e9",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# test that k is an integer\n",
+    "assert k==k-0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import MiniBatchKMeans\n",
+    "kmeans = MiniBatchKMeans(k, random_state=0)\n",
+    "kmeans.fit(data)\n",
+    "new_colors = kmeans.cluster_centers_[kmeans.predict(data)]\n",
+    "\n",
+    "ax = plot_pixels(data, colors=new_colors,\n",
+    "            title=f\"Reduced color space: {k} colors\")\n",
+    "\n",
+    "centers = kmeans.cluster_centers_\n",
+    "ax[0].scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);\n",
+    "for i in range(k):\n",
+    "    ax[0].text( centers[i, 0], centers[i, 1], f\"cluster {i}\")\n",
+    "\n",
+    "\n",
+    "ax[1].scatter(centers[:, 0], centers[:, 2], c='black', s=200, alpha=0.5);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's plot the original image side-by-side with the color quantized image.\n",
+    "\n",
+    "im_rgb_recolored = new_colors.reshape(im_rgb.shape)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n",
+    "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "ax[0].imshow(im_rgb)\n",
+    "ax[0].set_title('Original Image', size=16)\n",
+    "ax[1].imshow(im_rgb_recolored)\n",
+    "ax[1].set_title(f'{k}-color Image', size=16);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_kmeans = kmeans.predict(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.unique(y_kmeans)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_kmeans.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = y_kmeans.copy()\n",
+    "labels.shape = 318, 488\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6),\n",
+    "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "\n",
+    "cax = ax.imshow(labels, cmap=\"tab20b\")\n",
+    "ax.set_title('Cluster labels', size=16)\n",
+    "fig.colorbar(cax);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Q2 Determine which cluster ID (the label of the cluster) best detects the positive sample wells.\n",
+    "\n",
+    "Set this to the variable `best_cluster_id`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "d97254f3469b636b811eab266e22cb1f",
+     "grade": false,
+     "grade_id": "cell-aec1217b52730fa8",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "8cf265edc64be98fd38b0026b90bdf83",
+     "grade": true,
+     "grade_id": "cell-ecc1060220837c67",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# test that the answer is an integer.\n",
+    "assert best_cluster_id == best_cluster_id-0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "condition = y_kmeans == best_cluster_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binary = condition.copy()\n",
+    "binary.shape = 318, 488"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_rgb_recolored = new_colors.reshape(im_rgb.shape)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(16, 6),\n",
+    "                       subplot_kw=dict(xticks=[], yticks=[]))\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "ax[0].imshow(im_rgb)\n",
+    "ax[0].set_title('Original Image', size=16)\n",
+    "ax[1].imshow(binary)\n",
+    "ax[1].set_title(f'Binary Image', size=16);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further thoughts\n",
+    "\n",
+    "- LAMP is a potentially viable alternative to qPCR tests which would be much less expensive to implement. In fact, various efforts are underway to make LAMP-based SARS-CoV-2 testing a reality. See, e.g. [RTLAMP.org](https://www.rtlamp.org/), [Corona Detective: a simple, scalable, and robust SARS-CoV-2 detection method based on reverse transcription loop-mediated isothermal amplification](https://abrf.memberclicks.net/assets/JBT/September_2021_Early_Access/Corona%20Detective%20a%20simple%2C%20scalable%2C%20and%20robust%20SARS-CoV-2%20detection%20method%20based%20on.pdf), and many more.\n",
+    "\n",
+    "- How would you automatically determine the well number (e.g. \"A1\" or \"F3\") and the status of the result (\"positive\" or \"negative\") in a continuation of the above exercise?\n",
+    "\n",
+    "- The kids thank you for allowing their Nicolaus performance to happen safely!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/exercises/release/exercise-10/4__transcriptome_clustering_analysis.ipynb b/exercises/release/exercise-10/4__transcriptome_clustering_analysis.ipynb
new file mode 100644
index 0000000..0ac3da9
--- /dev/null
+++ b/exercises/release/exercise-10/4__transcriptome_clustering_analysis.ipynb
@@ -0,0 +1,798 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You must run this cell, but you can ignore its contents.\n",
+    "\n",
+    "import hashlib\n",
+    "\n",
+    "def ads_hash(ty):\n",
+    "    \"\"\"Return a unique string for input\"\"\"\n",
+    "    ty_str = str(ty).encode()\n",
+    "    m = hashlib.sha256()\n",
+    "    m.update(ty_str)\n",
+    "    return m.hexdigest()[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# transcriptome clustering analysis\n",
+    "\n",
+    "In this exercise, you are going to analyze the results of an experiment in which the RNA was sequenced (a \"transcriptome\" was made) for many cells in cell culture. We expect that the total number of cell types is rather limited, although we sequenced many individual cells.\n",
+    "\n",
+    "The data here is fake, but the analysis methods are real and are in heavy use across lots of different labs and can be applied to many other types of problems beyond RNA sequencing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.decomposition import PCA\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are given a dataset where the RNA expression levels of 50 genes from each of many cells was quantified. The data is in the file `RNAseq_data_50genes.csv`. Let's read this into a pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('RNAseq_data_50genes.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's have a first look at this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q1 Understanding the raw data.\n",
+    "\n",
+    "In the first row (with index 0), how many reads were made of gene 0? Put the answer in the variable `n_reads_sample0_gene0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "ad6d233c220d011a8bd8b0fa12801731",
+     "grade": false,
+     "grade_id": "cell-867448dc531b9577",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "743a3540105b97b337d7700a282cd341",
+     "grade": true,
+     "grade_id": "cell-927c0add61a309ae",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# This is a test of the above, do not change this code.\n",
+    "assert type(n_reads_sample0_gene0)==int\n",
+    "assert ads_hash(n_reads_sample0_gene0)=='1a5de96b83'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many total cells were sequenced? This is the number of rows in the dataframe. Put this in the variable `n_cells_sequenced`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "a1c0b029cd32314f47d53b102bddbf00",
+     "grade": false,
+     "grade_id": "cell-190def2eb393c7e4",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "615ee2939745eee7613193c187d2233e",
+     "grade": true,
+     "grade_id": "cell-c04f0d4317e6abfe",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# This is a test of the above, do not change this code.\n",
+    "assert type(n_cells_sequenced)==int\n",
+    "assert ads_hash(n_cells_sequenced)=='284b7e6d78'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many genes do we have in our dataset? This is actually the dimensionality of our dataset. We are counting reads for each of these genes, so if we have N genes, we have an N dimensional dataset. Put your answer in the variable `n_dim`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "71a387cc487a75a453e896365930054f",
+     "grade": false,
+     "grade_id": "cell-866e97e764ad0ed7",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "bcf3f56610fbee8e98eaa72fff2c864d",
+     "grade": true,
+     "grade_id": "cell-b28c7b470e97c1de",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# This is a test of the above, do not change this code.\n",
+    "assert type(n_dim)==int\n",
+    "assert ads_hash(n_dim)=='1a6562590e'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quickly plotting pandas DataFrames with seaborn\n",
+    "\n",
+    "Several lectures ago, we discussed seaborn we are are going to use it below to make a plot with our transcriptomic data. Let's first practice with a simple dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_df = pd.DataFrame(data={'column 1':[1,2,3,4,5], 'column 2':[1,5,5,2,2]})\n",
+    "display(simple_df)\n",
+    "sns.scatterplot(data=simple_df, x='column 1', y='column 2');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q2 Visualizing the raw data\n",
+    "\n",
+    "Now let's use seaborn to make a quick plot of the data (stored as a Pandas DataFrame in the variable `df`). Use the seaborn `scatterplot` function to make a plot like the following. Your plot should include the X and Y axes labels. You need only a single line of code for this.\n",
+    "\n",
+    "![scatterplot.png](scatterplot.png)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "9838c85a0ccbabda35f9eaa499854446",
+     "grade": true,
+     "grade_id": "cell-3eb44d7a59e553d8",
+     "locked": false,
+     "points": 1,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# first impressions of the data\n",
+    "\n",
+    "So, how does our data look? At first glance it looks... like a bunch of random numbers with no real structure! But could there be some structure? For example, above we learned that although many cells have been sequenced, we expect these are from only a very limited number of cell types.\n",
+    "\n",
+    "How can we figure out something about these cell types?\n",
+    "\n",
+    "Let's make use of principal component analysis (PCA) and clustering from scikit-learn as some of the first tools in our toolkit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Converting to plain numpy\n",
+    "\n",
+    "While Pandas is very convenient for many things, scikit learn uses plain numpy arrays and generally works best when the datatype is a floating point number rather than an integer. Let's do this conversion now and call our data `X`. (We also `copy()` this to a new numpy array to ensure it is contiguous in memory.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.to_numpy(dtype=np.float64).copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PCA\n",
+    "\n",
+    "Let's first run PCA on our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "pca = PCA().fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results of our analysis are stored in the variable `pca`. We can use this to project our original 50 dimensional data into its principle components and plot just the first two dimensions in this principle component space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projected = pca.transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(projected[:,0], projected[:,1],'.')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Structure in the PCA space\n",
+    "\n",
+    "Now, what do you notice about the data in this PCA space?\n",
+    "\n",
+    "Now, instead of looking like a structure-free blob, we seem to have some structure. What kind of structure do we have?\n",
+    "\n",
+    "\"3 clusters\", I hope you are thinking. Biologically speaking, we are now guessing that there were this many cell types in our original sample.\n",
+    "\n",
+    "## PCA explained variance\n",
+    "\n",
+    "One of the first questions about PCA is how much of the variance in our data are \"explained\" by the first N components of the projected data. Let's plot this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(np.cumsum(pca.explained_variance_ratio_),'.-')\n",
+    "plt.xlabel('number of components')\n",
+    "plt.ylabel('cumulative explained variance');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## mini batch K-Means\n",
+    "\n",
+    "Given that it looks like our data may have three clusters, let's find these clusters using mini batch K-Means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import MiniBatchKMeans\n",
+    "from sklearn.metrics.pairwise import pairwise_distances_argmin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Q3. Specifying *k*, the number of clusters\n",
+    "\n",
+    "As always, with a K-Means type algorithm, we must specify the number of clusters before running the algorithm. Use your thoughts from above and put this in the variable `n_clusters`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "ab3ee864e65ed95602e13840e4151fcd",
+     "grade": false,
+     "grade_id": "cell-956d77de44aaa49b",
+     "locked": false,
+     "schema_version": 3,
+     "solution": true,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# YOUR CODE HERE\n",
+    "raise NotImplementedError()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": false,
+    "editable": false,
+    "nbgrader": {
+     "cell_type": "code",
+     "checksum": "0c8e37abea8f9b3b8e824e2481bde6ac",
+     "grade": true,
+     "grade_id": "cell-b05818d9386980f7",
+     "locked": true,
+     "points": 1,
+     "schema_version": 3,
+     "solution": false,
+     "task": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# This is a test of the above, do not change this code.\n",
+    "assert type(n_clusters)==int\n",
+    "assert ads_hash(n_clusters)=='4e07408562'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we are going to actually run the algorithm from scikit learn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mbk = MiniBatchKMeans(n_clusters=n_clusters, batch_size=6, random_state=0, n_init='auto').fit(X);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plotting the clustering results in the original \"number of reads\" space\n",
+    "\n",
+    "Let's first plot the our raw read data in a scatter plot like above, but colored according to our cluster label. We will also plot our cluster centers here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mbk_means_cluster_centers = mbk.cluster_centers_\n",
+    "mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
+    "\n",
+    "x_gene_idx = 0\n",
+    "y_gene_idx = 1\n",
+    "\n",
+    "for k in range(n_clusters):\n",
+    "    my_members = mbk_means_labels == k\n",
+    "    cluster_center = mbk_means_cluster_centers[k]\n",
+    "    line, = ax.plot(X[my_members, x_gene_idx], X[my_members, 1], '.', markersize=5)\n",
+    "    ax.plot(cluster_center[x_gene_idx], cluster_center[1], 'o', markersize=10, markeredgecolor='black', markerfacecolor=line.get_color())\n",
+    "ax.set_xlabel('gene %d' % x_gene_idx)\n",
+    "ax.set_ylabel('gene %d' % y_gene_idx);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plotting the clustering results in PCA space\n",
+    "\n",
+    "Hmm, the plot above was not too informative. It does not seem to show obvious clusters in the data, and the points look very interwoven with others, at least for these two genes.\n",
+    "\n",
+    "Let's re-plot our cluster assignments, but this time using the projection into PCA space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
+    "\n",
+    "projected_centers = pca.transform(mbk.cluster_centers_)\n",
+    "\n",
+    "for k in range(n_clusters):\n",
+    "    my_members = mbk_means_labels == k\n",
+    "    projected_cluster_center = projected_centers[k]\n",
+    "    line, = ax.plot(projected[my_members, 0], projected[my_members, 1], '.',\n",
+    "                    markersize=1.8)\n",
+    "    ax.plot(projected_cluster_center[0], projected_cluster_center[1], 'o',\n",
+    "            markersize=10, markeredgecolor='black', markerfacecolor=line.get_color())\n",
+    "ax.set_xlabel('PC 1')\n",
+    "ax.set_ylabel('PC 2');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## silhouette analysis with K-means\n",
+    "\n",
+    "OK, so that last plot (in PCA space) is looking better. The automatically detected clusters seem to agree with the idea we had from just looking at the data. Let's now use a silhouette analysis as one way to check whether this was a particularly good number of clusters for this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
+    "from sklearn.cluster import KMeans\n",
+    "import matplotlib.cm as cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# From https://scikit-learn.org/0.21/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
+    "range_n_clusters = [2, 3, 4, 5, 6]\n",
+    "\n",
+    "for n_clusters in range_n_clusters:\n",
+    "    # Create a subplot with 1 row and 2 columns\n",
+    "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
+    "    fig.set_size_inches(18, 7)\n",
+    "\n",
+    "    # The 1st subplot is the silhouette plot\n",
+    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
+    "    # lie within [-0.1, 1]\n",
+    "    ax1.set_xlim([-0.1, 1])\n",
+    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
+    "    # plots of individual clusters, to demarcate them clearly.\n",
+    "    ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
+    "\n",
+    "    # Initialize the clusterer with n_clusters value and a random generator\n",
+    "    # seed of 10 for reproducibility.\n",
+    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10, n_init='auto')\n",
+    "    cluster_labels = clusterer.fit_predict(X)\n",
+    "\n",
+    "    # The silhouette_score gives the average value for all the samples.\n",
+    "    # This gives a perspective into the density and separation of the formed\n",
+    "    # clusters\n",
+    "    silhouette_avg = silhouette_score(X, cluster_labels)\n",
+    "    print(\"For n_clusters =\", n_clusters,\n",
+    "          \"The average silhouette_score is :\", silhouette_avg)\n",
+    "\n",
+    "    # Compute the silhouette scores for each sample\n",
+    "    sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
+    "\n",
+    "    y_lower = 10\n",
+    "    for i in range(n_clusters):\n",
+    "        # Aggregate the silhouette scores for samples belonging to\n",
+    "        # cluster i, and sort them\n",
+    "        ith_cluster_silhouette_values = \\\n",
+    "            sample_silhouette_values[cluster_labels == i]\n",
+    "\n",
+    "        ith_cluster_silhouette_values.sort()\n",
+    "\n",
+    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
+    "        y_upper = y_lower + size_cluster_i\n",
+    "\n",
+    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
+    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
+    "                          0, ith_cluster_silhouette_values,\n",
+    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
+    "\n",
+    "        # Label the silhouette plots with their cluster numbers at the middle\n",
+    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
+    "\n",
+    "        # Compute the new y_lower for next plot\n",
+    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
+    "\n",
+    "    ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
+    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
+    "    ax1.set_ylabel(\"Cluster label\")\n",
+    "\n",
+    "    # The vertical line for average silhouette score of all the values\n",
+    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
+    "\n",
+    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
+    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
+    "\n",
+    "    # 2nd Plot showing the actual clusters formed\n",
+    "    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
+    "    ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n",
+    "                c=colors, edgecolor='k')\n",
+    "\n",
+    "    # Labeling the clusters\n",
+    "    centers = clusterer.cluster_centers_\n",
+    "    # Draw white circles at cluster centers\n",
+    "    ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
+    "                c=\"white\", alpha=1, s=200, edgecolor='k')\n",
+    "\n",
+    "    for i, c in enumerate(centers):\n",
+    "        ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
+    "                    s=50, edgecolor='k')\n",
+    "\n",
+    "    ax2.set_title(\"The visualization of the clustered data.\")\n",
+    "    ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
+    "    ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
+    "\n",
+    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
+    "                  \"with n_clusters = %d\" % n_clusters),\n",
+    "                 fontsize=14, fontweight='bold')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, the average silhouette score was highest for 3 clusters. So it looks like our \"by eye\" analysis also agrees with this analysis.\n",
+    "\n",
+    "## agglomerative clustering\n",
+    "\n",
+    "Now let's try a different form of clustering in which we do not need to set, in advance, the number of clusters.\n",
+    "\n",
+    "This is *hierarchical agglomerative clustering*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import AgglomerativeClustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.hierarchy import dendrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aggclust = AgglomerativeClustering(distance_threshold=0, n_clusters=None)\n",
+    "aggclust.fit(X);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Based on example at https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html\n",
+    "def plot_dendrogram(model, **kwargs):\n",
+    "    # Create linkage matrix and then plot the dendrogram\n",
+    "\n",
+    "    # create the counts of samples under each node\n",
+    "    counts = np.zeros(model.children_.shape[0])\n",
+    "    n_samples = len(model.labels_)\n",
+    "    for i, merge in enumerate(model.children_):\n",
+    "        current_count = 0\n",
+    "        for child_idx in merge:\n",
+    "            if child_idx < n_samples:\n",
+    "                current_count += 1  # leaf node\n",
+    "            else:\n",
+    "                current_count += counts[child_idx - n_samples]\n",
+    "        counts[i] = current_count\n",
+    "\n",
+    "    linkage_matrix = np.column_stack([model.children_, model.distances_,\n",
+    "                                      counts]).astype(float)\n",
+    "\n",
+    "    # Plot the corresponding dendrogram\n",
+    "    dendrogram(linkage_matrix, **kwargs)\n",
+    "\n",
+    "plt.figure(figsize=(20,10))\n",
+    "plt.title('Hierarchical Clustering Dendrogram')\n",
+    "plot_dendrogram(aggclust)#, truncate_mode='level', p=3)\n",
+    "plt.xlabel(\"Number of points in node (or index of point if no parenthesis).\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These dendrograms are often used in combination with a display of the gene expression data in a \"heatmap\".\n",
+    "\n",
+    "Here is a single example of approximately zillions in the literature. The rows in the heatmap matrix are 1259 genes, the columns are different strains of bacteria.\n",
+    "\n",
+    "![ofv09303.jpg](ofv09303.jpg)\n",
+    "\n",
+    "Downloaded from [this paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4512144/) [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4512144/bin/ofv09303.jpg).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Further thoughts and questions\n",
+    "\n",
+    "- Does clustering before PCA work better or clustering after PCA?\n",
+    "- How would you judge this?\n",
+    "- As discussed in the lecture, PCA is a parameter-free, linear, very fast dimensionality reduction technique and is almost always the first dimensionality reduction technique you should try if you want to look at high dimensional data. If PCA doesn't perform well, there are many other techniques. PCA can give a good first intuition into dimensionality reduction as a broad class of techniques, which is why we do it here. Non-linear techniques in widespread use include ICA (independent component analysis), T-SNE (T stochastic neighbor embedding), and UMAP (Uniform Manifold Approximation and Projection)."
+   ]
+  }
+ ],
+ "metadata": {
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diff --git a/exercises/release/exercise-10/Ishihara_Plate_3.jpg b/exercises/release/exercise-10/Ishihara_Plate_3.jpg
new file mode 100644
index 0000000..9f94193
Binary files /dev/null and b/exercises/release/exercise-10/Ishihara_Plate_3.jpg differ
diff --git a/exercises/release/exercise-10/RNAseq_data_50genes.csv b/exercises/release/exercise-10/RNAseq_data_50genes.csv
new file mode 100644
index 0000000..86ed07f
--- /dev/null
+++ b/exercises/release/exercise-10/RNAseq_data_50genes.csv
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