Machine Learning: Models, Algorithms, and Reinforcement Learning

This source offers an extensive overview of machine learning concepts, beginning with supervised learning methods like linear regression, logistic regression, and generalized linear models (GLMs), which predict outcomes based on labeled data. It then explores generative learning algorithms such as Gaussian Discriminant Analysis (GDA) and Naive Bayes, which model data distribution for classification. The document further introduces kernel methods and Support Vector Machines (SVMs) for effective classification in high-dimensional spaces, followed by neural networksand deep learning architectures, including convolutional layers, and their training via backpropagation and stochastic gradient descent. Finally, it discusses crucial aspects of model generalization, including bias-variance tradeoff and the double descent phenomenon, along with regularization techniques and cross-validation to prevent overfitting. The text concludes by examining unsupervised learning with EM algorithms and Principal Component Analysis (PCA), Independent Component Analysis (ICA), and introduces reinforcement learning techniques like policy gradient (REINFORCE), Q-learning, and value function approximation, including LQR, and LQG.