Discussions

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.

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  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.

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  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]
  
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  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]
  
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  K-Means Clustering
  tl;dr: In this discussion section, we go over K-Means and K-Means ++ clustering.
  [Open in Colab] [Jupyter Notebook]
  
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  Hierarchical Clustering and GMMs
  tl;dr: In this discussion section, we go over Hierarchical clustering and GMMs
  [Open in Colab] [Jupyter Notebook]
  
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  Hierarchical Clustering and GMMs
  tl;dr: In this discussion section, we go over Hierarchical clustering and GMMs
  [Open in Colab] [Jupyter Notebook]
  
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  KNN
  tl;dr: In this discussion section, we go over K Nearest Neighbors
  [Open in Colab] [Jupyter Notebook]
  
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  Linear Regression
  tl;dr: In this discussion section, we go over Linear Regression
  [Open in Colab] [Jupyter Notebook]
  
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  Logistic Regression & Regularization
  tl;dr: In this discussion section, we go over Logistice Regression and Regularization
  [Open in Colab] [Jupyter Notebook]
  
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  Neural Networks
  tl;dr: In this discussion section, we go over Neural Networks in PyTorch.
  [Open in Colab] [Jupyter Notebook]
  
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  Recommender System
  tl;dr: In this discussion section, we go over Recommendation Systems.
  [Open in Colab] [Jupyter Notebook]