Machine Learning Fundamentals

This lecture introduces the fundamental concepts of machine learning, including statistical classification, algorithms, regularization, PCA, clustering, optimization, supervised learning, reinforcement learning, loss function, object recognition, decision tree, logistic regression, bias-variance tradeoff, ConvNet, artificial intelligence, data science, gradient boosting, neural networks, and regression. It covers various tasks and goals in machine learning, such as image recognition, cancer detection, image segmentation, distance estimation, pose estimation, image generation, style transfer, text generation, machine translation, automatic captions, speech synthesis, speech recognition, theorem proving, protein folding, and models of brain function.