Machine Learning Algorithms

Distinguish between Supervised and Unsupervised Learning, providing exam-relevant examples.

Define Concept Learning and its role as a theoretical foundation for supervised learning.

Define a Decision Tree, describe its structure, and outline its high-level learning process for classification.

Define Overfitting and Underfitting using a decision tree as an example, and explain how to recognize them in exam scenarios.

Explain the formulas for Entropy and Gini Impurity, and demonstrate their use in measuring node impurity for tree splits.

Define Ensemble Learning and explain the core ideas of Bagging and Boosting.

Describe how common ensemble methods like Random Forests and Gradient Boosting are presented in exam questions.

Explain the core concepts of Support Vector Machines (SVM), including the maximal margin, support vectors, and kernel trick, for an exam context.

Describe the architecture of an Artificial Neural Network (ANN), including neurons, layers, and activation functions.

Explain the purpose and conceptual steps of the Backpropagation algorithm for training an ANN in an exam context.