Phase 0 | U00.01 | Learning Path Orientation

How do mathematical ideas, data and model architectures come together to create AI? This learning-path orientation introduces the course's wider aim: understanding the mathematics and architectures behind AI, from the foundations through machine learning, deep learning, large language models and generative AI. The five orientation topics use visual stories, moving graphs and small tables to establish a shared language for the journey. We examine observations, predictions, model parameters, learning tasks and the flow of information, giving you a clear starting point for the mathematical and architectural ideas developed in later phases. In this lesson: • Observations, features, targets, labels, parameters and prediction errors • Regression, classification, clustering, representation learning and sequential decisions • Prediction rules, empirical distributions and simulated outcomes • Training, validation, testing and information leakage • Supervised, unsupervised and self-supervised learning; deep learning and generative AI Helpful background: No prior AI, mathematics or programming knowledge is assumed. U00.01 | Mathematics and Architectures Behind AI Phase 0: Readiness and Orientation Next in the foundation series: U00.02 — Arithmetic Readiness Bridge. Use it to refresh the numerical ideas you need. CHAPTERS 00:00 Begin with a question a person can understand 01:32 One observation is one recorded occasion 03:11 Features, targets and labels are roles 04:58 A prediction and an observation are different objects 06:40 A small model has adjustable parts 08:17 A model family, a fitted model and a learning procedure 09:58 Measure an error before giving it a technical name 11:53 Training success is not the final question 13:38 Use the explanation without treating it as a guarantee 15:15 Regression asks for a quantity 16:44 Classification asks for a category 18:34 Clustering asks for a grouping 20:06 Density or distribution estimation asks where outcomes occur 21:52 Representation learning asks for a useful way to describe data 23:33 Sequential decisions ask what to do across several steps 25:24 Choose the question before choosing a method 27:07 Where the whole course is going 29:07 What it means to approximate a rule 30:48 Follow a fixed input through a fixed rule 32:29 Change a parameter and watch several predictions change 33:58 Read a short formula without needing algebra first 35:37 Build an empirical distribution from six outcomes 37:22 The same point prediction can hide different possibilities 39:00 A distribution can describe everything, or a selected situation 40:53 Generate a simulated outcome and keep its identity clear 42:33 Connect the two formulations without losing their difference 44:06 Read the table with a recording contract 45:50 Separate a stored record from the model input 47:14 Change the target without secretly leaking its answer 48:48 Repeated, missing and zero are three different meanings 50:19 Training, validation and a final test have different jobs 52:11 Information can be unavailable even when a file contains it 53:45 An observation need not look like a spreadsheet row 55:11 Check whether the selected inputs can distinguish the cases 56:38 What a tiny table can establish 58:06 Supervised learning uses examples with the target supplied 59:33 Unsupervised learning still has a goal and assumptions 1:01:09 Self-supervised learning derives a training target from the data 1:02:44 Keep a few related terms distinct 1:04:17 Generative goals concern producing outcomes or content 1:05:55 These are overlapping descriptions, not four sealed boxes 1:07:32 Data science, machine learning, deep learning and generative AI 1:09:07 Put the whole learning workflow together 1:10:44 Choose a starting route without turning support into a barrier 1:12:27 Explain the story yourself and ask the next useful question Data Meets Theory explores mathematics, coding and the ideas behind data science, machine learning, deep learning and generative AI. Subscribe to follow the course, one idea at a time. #MathematicsOfAI #AIArchitecture #TheoryMeetsData