A typical machine learning curriculum should cover the following topics:

Introduction to machine learning

Linear Regression

Logistic Regression

Decision Trees and Random Forests

Naive Bayes

k-Nearest Neighbors (k-NN)

Support Vector Machines (SVMs)

Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Clustering (K-means, Hierarchical)

Dimensionality Reduction (PCA, t-SNE)

Ensemble Methods

Model evaluation and selection

Hyperparameter tuning

Regularization

Bias-Variance Trade-off

Overfitting and Underfitting

Model interpretability and explainability