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UBC-CS · Oct 31, 2023 · 0afc91e · 0afc91e
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diff --git a/_sources/lectures/14_K-Means.ipynb b/_sources/lectures/14_K-Means.ipynb
@@ -48,7 +48,6 @@
     "    - Choosing K ([video](https://youtu.be/M5ilrhcL0oY))\n",
     "- iClicker questions (~10 mins)     \n",
     "- Break (~5 mins)\n",
-    "- Introduction to GMMs (~15 mins)\n",
     "- Demo: Clustering images (~15 mins)\n",
     "- Final comments and summary (~5 mins)"
    ]
@@ -126,9 +125,6 @@
     "- Broadly explain the K-Means algorithm apply `sklearn`'s `KMeans` algorithm.  \n",
     "- Point out pros and cons of K-Means and the difficulties associated with choosing the right number of clusters.   \n",
     "- Create the Elbow plot and Silhouette plots for a given dataset. \n",
-    "- Broadly explain the idea of Gaussian mixture models (GMMs) and the differences between K-Means and GMMs.   \n",
-    "- Broadly explain the covariance type hyperparameter of GMMs. \n",
-    "- Broadly explain model selection in GMMs. \n",
     "- Broadly explain the difference between soft cluster assignment vs. hard cluster assignment.  \n",
     "- Use clustering for the problem of image clustering and interpret clusters. \n",
     "- Demonstrate how input data representation can influence clustering results. "
@@ -3042,19 +3038,6 @@
     "- Two popular ways to provide insight into how many clusters are reasonable for the give problem are: **the Elbow method** and **the Silhouette method**.  "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Clustering with Gaussian mixture models**\n",
-    "\n",
-    "- Gaussian mixture models model the distribution of data as a mixture of $k$ Gaussian components. \n",
-    "- Each Gaussian has a different mean vector and co-variance matrix and a weight associated with it, which control its location, shape, and complexity. \n",
-    "- You can control and shape and complexity of the Gaussians by restricting the covariance matrix. There are four common ways to do this in `sklearn`: spherical, diag, tied, full \n",
-    "- Gaussian mixture models are a probabilistic model; they assign a probability to each data point belonging to each cluster. In `sklearn`, we can access these soft assignments using `predict_proba` scores. \n",
-    "- The model also gives us the log likelihood of fitting the data. It's possible to calculate AIC and BIC scores and pick the number of components where these scores are the lowest. "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {