You can download the discussions here. We will try to upload discussion material prior to their corresponding sessions. Future discussions listed below likely have broken links.

  • Optional discussion.
    tl;dr: This session is optional since we haven't had lecture yet. Feel free to come and meet the TA.
    [slides]

    Helpful other resources here.

  • Pandas and Scikit-Learn Example
    tl;dr: In this discussion section, we go over an example with another dataset using Pandas and Scikit-Learn.
    [Open in Colab] [Jupyter Notebook] [CSV data file]
  • Linear Algebra and Probability
    tl;dr: In this discussion section, we go over practical applications of probability and linear algebra.
    [Open in Colab] [Jupyter Notebook] [CSV data file]
  • K-Means Clustering
    tl;dr: In this discussion section, we go over K-Means and K-Means ++ clustering.
    [Open in Colab] [Jupyter Notebook]
  • Hierarchical Clustering and GMMs
    tl;dr: In this discussion section, we go over Hierarchical clustering and GMMs
    [Open in Colab] [Jupyter Notebook]
  • Hierarchical Clustering and GMMs
    tl;dr: In this discussion section, we go over Hierarchical clustering and GMMs
    [Open in Colab] [Jupyter Notebook]
  • KNN
    tl;dr: In this discussion section, we go over K Nearest Neighbors
    [Open in Colab] [Jupyter Notebook]
  • Linear Regression
    tl;dr: In this discussion section, we go over Linear Regression
    [Open in Colab] [Jupyter Notebook]
  • Logistic Regression & Regularization
    tl;dr: In this discussion section, we go over Logistice Regression and Regularization
    [Open in Colab] [Jupyter Notebook]