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+    "## Bonus Assignments\n",
+    "<ul>\n",
+    "<li> What is the disadvantages of the KNN classifier</li>\n",
+    "1. Does not work well with large dataset:\n",
+    "In large datasets, the cost of calculating the distance between the new point and each existing points is huge which degrades the performance of the algorithm.\n",
+    "\n",
+    "2. Does not work well with high dimensions:\n",
+    "The KNN algorithm doesn't work well with high dimensional data because with large number of dimensions, it becomes difficult for the algorithm to calculate the distance in each dimension.\n",
+    "\n",
+    "3. Need feature scaling:\n",
+    "We need to do feature scaling (standardization and normalization) before applying KNN algorithm to any dataset. If we don't do so, KNN may generate wrong predictions.\n",
+    "\n",
+    "4. Sensitive to noisy data, missing values and outliers:\n",
+    "KNN is sensitive to noise in the dataset. We need to manually impute missing values and remove outliers.\n",
+    "<li> How to optimize the KNN algorithm</li>\n",
+    "for a given test sample x:\n",
+    "\n",
+    "   - find K most similar samples from training set, according to similarity measure s\n",
+    "\n",
+    "   - return the majority vote of the class from the above set\n",
+    "   \n",
+    "Consequently the only thing used to define KNN besides K is the similarity measure s, and that's all. There is literally nothing else in this algorithm (as it has 3 lines of pseudocode). On the other hand finding \"the best similarity measure\" is equivalently hard problem as learning a classifier itself, thus there is no real method of doing so, and people usually end up using either simple things (Euclidean distance) or use their domain knowledge to adapt s to the problem at hand.\n",
+    "</ul>"
+   ]
+  }
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