MLG 004 Algorithms - Intuition

Try a walking desk to stay healthy while you study or work! Show notes at ocdevel.com/mlg/4 The AI Hierarchy • Artificial Intelligence is divided into subfields such as reasoning, planning, and learning. • Machine Learning is the learning subfield of AI. • Machine learning consists of three phases: Predict (Infer) Error (Loss) Train (Learn) • Core Intuition • An algorithm makes a prediction. • An error function evaluates how wrong the prediction was. • The model adjusts its internal weights (training) to improve. Example: House Price Prediction • Input: Spreadsheet with features like bedrooms, bathrooms, square footage, distance to downtown. • Output: Predicted price. • The algorithm iterates over data, learns patterns, and creates a model. • A model • = algorithm + learned weights. Features • = individual columns used for prediction. Weights • = coefficients applied to each feature. • The process mimics algebra: rows = equations, entire spreadsheet = matrix. • Training adjusts weights to minimize error. Feature Types Numerical • : e.g., number of bedrooms. Nominal (Categorical) • : e.g., yes/no for downtown location. • Feature engineering can involve transforming raw inputs into more usable formats. Linear Algebra Connection • Machine learning uses linear algebra to process data matrices. • Each row is an equation; training solves for best-fit weights across the matrix. Categories of Machine Learning 1. Supervised Learning • Algorithm is explicitly trained with labeled data (e.g., price of a house). • Examples: Regression • (predicting a number): linear regression Classification • (predicting a label): logistic regression • 2. Unsupervised Learning • No labels are given; the algorithm finds structure in the data. • Common task: clustering • (e.g., user segmentation for ads). • Learns patterns without predefined classes. 3. Reinforcement Learning • Agent takes actions in an environment to maximize cumulative reward. • Example: mouse in a maze trying to find cheese. • Includes rewards (+points for cheese) and penalties (–points for failure or time). • Learns policies for optimal behavior. • Algorithms: Deep Q-Networks, policy optimization. • Used in games, robotics, and real-time decision systems. Terminology Recap Algorithm • : Code that defines a learning strategy (e.g., linear regression). Model • : Algorithm + learned weights (trained state). Features • : Input variables (columns). Weights • : Coefficients learned for each feature. Matrix • : Tabular representation of input data. Learning Path and Structure • Machine learning is a subfield of AI. • Machine learning itself splits into: • Supervised Learning • Unsupervised Learning • Reinforcement Learning • • Each category includes multiple algorithms. Resources MachineLearningMastery.com • : Accessible articles on ML basics. The Master Algorithm • by Pedro Domingos: Introductory audio-accessible book on ML. • Podcast’s own curated learning paths: ocdevel.com/mlg/resources