Advanced Statistics (CE) - Fall 2025

Topic outline

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  • Select activity Announcements

    

    Announcements Forum

    Class Meets from 1:30-3:00 in Z138 EXCEPT for 11/12/2025, where class will meet in Z104.
  • Select activity Syllabus

    

    Syllabus File

    Please review and do all prep work/downloading prior to class start.

• November 10, 2025
  • Select activity 01. Data Basics (before class)

    

    01. Data Basics (before class) File

    • Basic NumPy operations
    • Visualizing distributions with histograms and KDEs
    • Statistical summaries and outlier detection
    • Distribution transformations
  • Select activity 01. Data Basics (after class)

    

    01. Data Basics (after class) File

• November 12, 2025 - Z104 today only
  • Select activity 02. Statistics Basics (before class)

    

    02. Statistics Basics (before class) File

• November 17, 2025
  • Select activity 03. Multidimensional Data (before class)

    

    03. Multidimensional Data (before class) File

• November 19, 2025
  • Select activity 04. Likelihood Maximization (slides)

    

    04. Likelihood Maximization (slides) File
  • Select activity 04. Likelihood Maximization (after class)

    

    04. Likelihood Maximization (after class) File

• November 24, 2025

• December 1, 2025
  • Select activity 06. MNIST: logistic regression, k-means, kNN

    

    06. MNIST: logistic regression, k-means, kNN File
  • Select activity 06. MNIST (after class)

    

    06. MNIST (after class) File

• December 3, 2025

• December 8, 2025
  • Select activity eMNIST slice

    

    eMNIST slice File
  • Select activity 08. eMNIST tSNE UMAP

    

    08. eMNIST tSNE UMAP File

• December 10, 2025
  • Select activity Dataset Modeling

    

    Dataset Modeling Assignment

    Opened: Wednesday, December 10, 2025, 12:00 AM

    Due: Friday, December 12, 2025, 11:59 PM

    Your goal is to train a model on the train split of the dataset, and predict the target variable in the dataset on the test split.

    - Examine the origins of your datasets. Are your features interpretable? Do you have any hypothesis about their relative importance? Do the features tend to be correlated or not?

    - Do you expect the dataset to be homogeneous or heterogeneous? How can you best visualize the variance of the dataset?

    - Do you expect to have a significant portion of outliers? How would you filter them?

    - Does the target variable look like it can be explained by the predictor variables? Can you visualize it?

    - Once you have trained a model, how can you assess its performance? Where does it perform better, where does it perform worse? Which features ended up being important for the model?

    
    

  • Select activity 09. Example dataset

    

    09. Example dataset File