Overview of machine learning algorithms. Infer/predict -> error/loss -> train/learn. Supervised, unsupervised, reinforcement learning. ## Resources - Tour of Machine Learning Algorithms (http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms) `article:easy` - The Master Algorithm (http://amzn.to/2kLOQjW) `audio:medium` Semi-technical overview of ML basics & main algorithms ## Episode Learning (ML) - 3-step process ** Infer / Predict ** Error / Loss ** Train / Learn - First as batch from spreadsheet, then "online" going forward ** Pre-train your "model" ** "Examples" ** "Weights" - Housing cost example ** "Features" ** Infer cost based on num_rooms, sq_foot, etc ** Error / Loss function Categories - Supervised learning ** Vision (CNN) ** Speech (RNN) - Unsupervised ** Market segmentation - Reinforcement & Semi-Supervised ** Planning (DQN): Games (chess, Mario); Robot movement |