Machine Learning Basics

• Linear and Logistic Regression
• Decision Trees (E), Random Forests (E) and Support Vector Machine (SVM) (CE)
• Clustering (K-means) : Unsupervised Learning (C)
• Dimensionality Reduction : PCA (CE)