num_pages: 97
description: Table of ContentsPart 1: Introduction. The big picture of arti cial intelligence and machine learningpast present and future.Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression loss functions over tting and gradient descent.Part 2.2: Supervised Learning II. Two methods of classi cation: logistic regression and support vector machines (SVMs).Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors decision trees random forests. Introducing cross-validation hyperparameter tuning and ensemble models.Part 3: Unsupervised Learning. Clustering: k-means hierarchical. Dimensionality reduction: principal components analysis (PCA) singular value decomposition (SVD).Part 4: Neural Networks &amp; Deep Learning. Why where and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs) recurrent neural networks (RNNs). Real-world applications.Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision processes. Q-learning policy learning and deep reinforcement learning. The value learning problem.Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum.
language: English
title: Machine Learning for Humans
publisher: None