tanjascats/ML-teaching-materials

Collection of resources for teaching Machine Learning lectures with an emphasis on interactive demos of ML algorithms

ML teaching materials

Algotithm demos

• Streamlit playground with multiple algorithms (source)
  • model types: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Neural Network, K Nearest Neighbors, Gaussian Naive Bayes, SVC
  • demo of decision boundary, accuracy/F1 score on test set, general tips on algorithms
  • hyperparameter influence, dataset distribution influence

KNN

• algorithm steps
• influence of the hyperparameters
• decision boundary

Decision Tree

• great visuals for intuition on building the DTs
• demo with Jupyter widgets

Gradient Boosting

• Regression
• Classification
• StatQuest - the whole YT channel has good videos

Random Forest

• by Andrej Karpathy

SVM

• by Andrej Karpathy

Linear and Logistic Regression

• calculator
• gradient descent demo
• linear regression
• gradient descent - nice mathematical exmplanation; contains examples of a trajectory of parameters depending on different learning rates

Perceptron

• Perceptron
• Iteration animation

NNs

• by Andrej Karpathy
• Tensorflow Playground
• phiresky@github
• CNN explainer
• DL tutorial by Thomas Lidy

Other (blogs, etc.)

• Brilliantly wrong
• R2D3
• Hyperparameter optimisation app