Resources

General resources

Add reusable readings, software setup instructions, and support links here.

Use accessible link text and remove this page if the course does not need shared resources.

EDA Materials

  • check/include overview: https://ds100.org/course-notes/visualization-2/
  • for EDA/best practices in visualization: https://harvard-iacs.github.io/2019-CS109A/lectures/lecture9/presentation/Lecture9a_Visualization.pdf
  • https://proceedings.scipy.org/articles/tyyd7356

Regression

  • Example: https://www.tidymodels.org/start/models/

  • MAFS6010u_Regression see pairwise scatter and multiple linear regression.ipynb

  • Check: https://github.com/lmarti/machine-learning/blob/master/02.%20Linear%20regression.ipynb

  • Nice introduction: https://kuleshov-group.github.io/aml-website (careful: copyright!)

  • Lecture: https://harvard-iacs.github.io/2019-CS109A/lectures/lecture7/presentation/Lecture7_ModeLSelectionRegularization.pdf

  • https://github.com/kuleshov/cornell-cs5785-2025-applied-ml/blob/main/lecture-notes/lecture2-supervised-learning.ipynb

  • https://jose.theoj.org/papers/10.21105/jose.00239 -> slides not useful for ABD -> Exercises: penguin dataset, but maybe useful as an inspiration.

  • https://torch.mlverse.org/

  • instructive animations on neural networks etc.: https://www.3blue1brown.com/topics/neural-networks

  • Andrew Ng lecture: https://www.youtube.com/watch?v=jGwO_UgTS7I

  • https://cs229.stanford.edu/

Logistic Regression

  • good slides: https://harvard-iacs.github.io/2019-CS109A/lectures/lecture10/ (https://harvard-iacs.github.io/2019-CS109A/lectures/lecture11/presentation/Lecture11_LogReg2.pdf)
  • good (simple; geographical) explanation: https://www.youtube.com/watch?v=yIYKR4sgzI8
  • Lecture, Ng: https://www.youtube.com/watch?v=4u81xU7BIOc
  • https://web.stanford.edu/class/cme250/files/cme250_lecture2.pdf
  • https://slds-lmu.github.io/i2ml/chapters/03_supervised_classification/03-04-classification-logistic/
  • https://ubc-cs.github.io/cpsc330-2023W1/README.html
  • Optional /extension: changing the data: https://ubc-cs.github.io/cpsc330-2023W1/lectures/09_classification-metrics.html#optional-changing-the-data
  • https://harvard-iacs.github.io/2019-CS109A/lectures/lecture-11/notebook/
  • https://github.com/UBC-CS/cpsc330-2023W1
  • https://slds-lmu.github.io/i2ml/
  • https://www2.stat.duke.edu/courses/Spring20/sta210.001/labs/lab-08-logistic.html

ML

  • Lectures: https://harvard-iacs.github.io/2019-CS109A/lectures/lecture15/presentation/Lecture15_Decision_Trees.pdf
  • https://medium.com/data-science/entropy-how-decision-trees-make-decisions-2946b9c18c8

ML Text

  • Check for NLP assignments: https://github.com/microsoft/ai-for-beginners
  • https://juliasilge.github.io/tidytext/

Neural Networks

Organizational deployment

  • Ethics/Fairness: https://developers.google.com/machine-learning/crash-course/fairness

  • https://github.com/cs124/labs/blob/main/Lab2_LogisticRegression_Solutions.md#ethics-discussion

  • Interpretable Machine Learning (Book)

AI for generating and understanding analytical code.

  • https://recipes.tidymodels.org/reference/step_dummy.html - see dummy-variable encoding
  • https://www.datacamp.com/datalab
  • https://www.datacamp.com/datalab/ai
  • Connect to AGI debates (jagged frontier…), which unlocks generation, but places new burdens on validation and testing
  • Reliability: monitoring/ensuring that the data input remains stable/in-line with the model (e.g., one-hot encoded variables with the specific levels for regression)
  • Fairness: https://developers.google.com/machine-learning/crash-course/fairness
  • https://rodneybrooks.com/rodney-brooks-three-laws-of-artificial-intelligence