Machine Learning

Topics covered

• Foundations: Probability, information theory, linear algebra, and optimization.
• Supervised learning: Linear and logistic regression, support vector machines, tree-based methods, and neural networks including CNNs and RNNs.
• Model evaluation: Overfitting, underfitting, the bias–variance tradeoff, and performance evaluation.
• Unsupervised learning: K-means, Gaussian mixture models, and expectation-maximization.